U.S. patent application number 13/657517 was filed with the patent office on 2013-07-11 for dual window processing schemes for spectroscopic optical coherence tomography (oct) and fourier domain low coherence interferometry.
This patent application is currently assigned to Duke University. The applicant listed for this patent is Duke University. Invention is credited to Robert N. Graf, Francisco E. Robles, Adam Wax.
Application Number | 20130176560 13/657517 |
Document ID | / |
Family ID | 44307263 |
Filed Date | 2013-07-11 |
United States Patent
Application |
20130176560 |
Kind Code |
A1 |
Wax; Adam ; et al. |
July 11, 2013 |
DUAL WINDOW PROCESSING SCHEMES FOR SPECTROSCOPIC OPTICAL COHERENCE
TOMOGRAPHY (OCT) AND FOURIER DOMAIN LOW COHERENCE
INTERFEROMETRY
Abstract
Current apparatuses and methods for analysis of spectroscopic
optical coherence tomography (SOCT) signals suffer from an inherent
tradeoff between time (depth) and frequency (wavelength)
resolution. In one non-limiting embodiment, multiple or dual window
(DW) apparatuses and methods for reconstructing time-frequency
distributions (TFDs) that applies two windows that independently
determine the optical and temporal resolution is provided. For
example, optical resolution may relate to scattering information
about a sample, and temporal resolution may be related to
absorption or depth related information. The effectiveness of the
apparatuses and methods is demonstrated in simulations and in
processing of measured OCT signals that contain fields which vary
in time and frequency. The DW technique may yield TFDs that
maintain high spectral and temporal resolution and are free from
the artifacts and limitations commonly observed with other
processing methods.
Inventors: |
Wax; Adam; (Chapel Hill,
NC) ; Graf; Robert N.; (Durham, NC) ; Robles;
Francisco E.; (Durham, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Duke University; |
Durham |
NC |
US |
|
|
Assignee: |
Duke University
Durham
NC
|
Family ID: |
44307263 |
Appl. No.: |
13/657517 |
Filed: |
October 22, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13574484 |
Feb 13, 2013 |
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PCT/US2011/022271 |
Jan 24, 2011 |
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13657517 |
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61297588 |
Jan 22, 2010 |
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Current U.S.
Class: |
356/300 ;
356/342 |
Current CPC
Class: |
G01B 9/02084 20130101;
G01J 2003/4538 20130101; G01N 21/49 20130101; G01B 9/02091
20130101; G01J 3/453 20130101; G01B 9/02044 20130101; G01B 9/02087
20130101; G01J 3/45 20130101 |
Class at
Publication: |
356/300 ;
356/342 |
International
Class: |
G01J 3/45 20060101
G01J003/45 |
Goverment Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH/DEVELOPMENT
[0010] This invention was supported by the National Institute of
Health, Grant No. R121-CA-120128, and the National Science
Foundation, Grant No. BES-03-48204. The United States Government
has certain rights in the invention.
Claims
1. A method of obtaining depth-resolved spectra of a sample for
determining scattering and absorption characteristics within the
sample, comprising: emitting a beam onto a splitter, wherein the
splitter splits light from the beam to produce a reference beam,
and an input beam to the sample; cross-correlating the reference
beam with a sample beam returned from the sample as a result of the
input beam by mixing the reference beam and the returned sample
beam from the sample to yield a cross-correlated profile having
optical, depth-resolved information about the returned sample beam;
generating a spectroscopic depth-resolved profile that includes
optical properties about the sample by: providing first one or more
spectroscopic windows of the cross-correlated profile, each of the
first one or more spectroscopic windows having a first width at a
given center wavelength to obtain optical information about the
sample for each given center wavelength; applying a Fourier
transform to the optical information about the sample to recover
high resolution optical information about the sample at each given
center wavelength simultaneously; providing second one or more
spectroscopic windows of the cross-correlated profile, each of the
second one or more spectroscopic windows having a second width
greater than the first width at a given center wavelength to obtain
absorption information about the sample for each given center
wavelength; applying a Fourier transform to the absorption
information about the sample as a function of depth to recover high
resolution depth information about the sample at each given center
wavelength simultaneously; and co-registering the high resolution
optical information and the high resolution depth information about
the sample to yield a single high resolution spectroscopic
optical-resolved, depth-resolved profile about the sample.
2. The method of claim 1, further comprising recovering scattering
information about the sample from the spectroscopic depth-resolved
profile.
3. The method of claim 2, wherein recovering the scattering
information is obtained by measuring a frequency of a spectral
modulation in the spectroscopic depth-resolved profile.
4. The method of claim 2, wherein recovering the scattering
information is obtained by comparing the spectroscopic
depth-resolved profile to a predicted analytical or numerical
distribution of the sample.
5. The method of claim 1, wherein providing the first one or more
spectroscopic windows is comprised of providing a first one or more
Gaussian windows, a first one or more multiple simultaneous
windows, or a first one or more other windows.
6. The method of claim 1, wherein providing the second one or more
spectroscopic windows is comprised of providing a second one or
more Gaussian windows, a second one or more multiple simultaneous
windows, or a second one or more other windows.
7. The method of claim 1, wherein the splitter is comprised from
the group consisting of a beam splitter and an optical fiber
splitter.
8. The method of claim 1, wherein emitting a beam onto the splitter
comprises emitting a collimated beam.
9. The method of claim 8, wherein the input beam comprises a
collimated beam.
10. The method of claim 8, wherein the reference beam comprises a
collimated beam.
11. The method of claim 1, wherein the beam is comprised of one of
a light comprised of white light from an arc lamp or thermal source
and a super continuum laser.
12. The method of claim 1, wherein cross-correlating the reference
beam with the returned sample beam comprises determining an
interference term by measuring the intensity of the returned sample
beam and the reference beam independently and subtracting them from
the total intensity of the returned sample beam.
13. The method of claim 1, wherein the reference beam is reflected
off of a reference mirror.
14. The method of claim 1, wherein the length of the path of the
reference beam is fixed.
15. The method of claim 1, wherein the splitter is attached to a
fixed reference arm.
16. The method of claim 1, wherein the sample is attached to a
fixed sample arm.
17. The method of claim 1, wherein the bandwidth of the first one
or more spectroscopic windows is approximately 0.3
micrometers.sup.-1 (um).
18. The method of claim 1, wherein the bandwidth of the second one
or more spectroscopic windows is between approximately 0.8
micrometers.sup.-1 (um).
19. The method of claim 1, wherein the returned sample beam is
comprised of a scattered sample beam comprised of scattered light
from scatterers in the sample.
20. The method of claim 19, further comprising spectrally
dispersing the mixed reference beam and the scattered sample beam
to yield a spectrally-resolved, depth-resolved cross-correlated
profile having depth-resolved information about the scattered
sample beam.
21. The method of claim 1, further comprising dispersing the mixed
reference beam and the scattered sample beam using a
spectrograph.
22. The method of claim 19, in which the scatterers are cell
nuclei.
23. The method of claim 20, wherein the high resolution optical
information is comprised of spectral information about the sample
at each given center wavelength.
24. The method of claim 23, further comprising comparing the high
resolution spectral information to known spectrum of one or more
biological absorbers.
25. The method of claim 24, wherein the one or more biological
absorbers comprises one or more contrast agents.
26. The method of claim 24, wherein the one or more biological
absorbers are comprised of one or more particles.
27. The method of claim 24, wherein the one or more biological
absorbers are comprised of nano-particles.
28. The method of claim 23, further comprising separating the high
resolution spectral information into one or more color
channels.
29. An apparatus for obtaining depth-resolved information of a
sample in order to determine the scattering and absorption
characteristics within the sample, comprising: a receiver adapted
to receive a reference beam and a returned sample beam containing
light returned from a sample in response to the sample receiving a
sample beam, wherein the receiver is further adapted to
cross-correlate the reference beam with the returned sample beam; a
detector adapted to detect the cross-correlated reference beam and
the returned sample beam to yield a cross-correlated profile having
depth-resolved information about the returned sample beam; and a
processor unit adapted to generate a spectroscopic depth-resolved
profile that includes optical properties about the sample by:
providing first one or more spectroscopic windows of the
cross-correlated profile, each of the first one or more
spectroscopic windows having a first width at a given center
wavelength to obtain optical information about the sample for each
given center wavelength; applying a Fourier transform to the
optical information about the sample as a function of wavelength to
recover high resolution optical information about the sample at
each given center wavelength simultaneously; providing second one
or more spectroscopic windows of the cross-correlated profile, each
of the second one or more spectroscopic windows having a second
width greater than the first width at a given center wavelength to
obtain absorption information about the sample for each given
center wavelength; applying a Fourier transform to the absorption
information about the sample as a function of depth to recover high
resolution depth information about the sample at each given center
wavelength simultaneously; and co-registering the high resolution
optical information and the high resolution depth information about
the sample to yield a single high resolution spectroscopic
optical-resolved, depth-resolved profile about the sample.
30. The apparatus of claim 29, wherein the processor unit is
further adapted to recover scattering information about the sample
from the spectroscopic depth-resolved profile.
31. The apparatus of claim 29, wherein the processor unit is
further adapted to recover scattering information by measuring a
frequency of a spectral modulation in the spectroscopic
depth-resolved profile.
32. The apparatus of claim 29, wherein the processor unit is
further adapted to recover scattering information by comparing the
spectroscopic depth-resolved profile to a predicted analytical or
numerical scattering distribution of the sample.
33. The apparatus of claim 29, wherein providing the first one or
more spectroscopic windows is comprised of providing a first one or
more Gaussian windows, a first one or more multiple simultaneous
windows, or a first one or more other window.
34. The apparatus of claim 29, wherein providing the second one or
more spectroscopic windows is comprised of providing a second one
or more Gaussian windows, a first one or more multiple simultaneous
windows, or a second one or more other window.
35. The apparatus of claim 29, wherein the receiver is comprised of
a splitter.
36. The apparatus of claim 35, wherein the splitter is comprised
from the group consisting of a beam splitter and an optical fiber
splitter.
37. The apparatus of claim 29, wherein the sample beam comprises a
collimated beam.
38. The apparatus of claim 29, wherein the reference beam comprises
a collimated beam.
39. The apparatus of claim 29, wherein the received beam is
comprised of one of a light comprised from the group consisting of
a white light generated by an arc lamp or thermal source, and a
super continuum laser.
40. The apparatus of claim 29, wherein a length of a path of the
reference beam is fixed.
41. The apparatus of claim 29, wherein the receiver is attached to
a fixed reference arm.
42. The apparatus of claim 29, wherein the sample is attached to a
fixed sample arm.
43. The apparatus of claim 29, wherein the detector is comprised of
a dispersive element.
44. The apparatus of claim 43, wherein the dispersive element is a
spectrograph.
45. The apparatus of claim 29, wherein the bandwidth of the first
one or more spectroscopic windows is approximately 0.3
micrometers.sup.-1 (um).
46. The apparatus of claim 29, wherein the bandwidth of the second
one or more spectroscopic windows is approximately 0.8
micrometers.sup.-1 (um).
47. The apparatus of claim 29, wherein the returned sample beam is
comprised of a scattered sample beam comprised of scattered light
from scatterers in the sample.
48. The apparatus of claim 47, wherein the detector is adapted to
spectrally disperse the mixed reference beam and the scattered
sample beam to yield a spectrally-resolved cross-correlated profile
having depth-resolved information about the scattered sample
beam.
49. The apparatus of claim 47, in which the scatterers are cell
nuclei.
50. The apparatus of claim 47, wherein the high resolution optical
information is comprised of high resolution spectral information
about the sample at each given center wavelength.
51. The apparatus of claim 50, wherein the processing unit is
further adapted to compare the high resolution spectral information
about the sample at each given center wavelength to known spectrum
of one or more biological absorbers.
52. The apparatus of claim 51, wherein the one or more biological
absorbers comprises one or more contrast agents.
53. The apparatus of claim 51, wherein the one or more biological
absorbers are comprised of one or more particles.
54. The apparatus of claim 51, wherein the one or more biological
absorbers are comprised of nano-particles.
55. The apparatus of claim 50, wherein the processing unit is
further adapted to separate the high resolution spectral
information into one or more color channels.
56. The method of claim 1, wherein the optical information includes
scattering information about the sample.
57. The apparatus of claim 29, wherein the optical information
includes scattering information about the sample.
Description
RELATED APPLICATIONS
[0001] The present application is a continuation-in-part
application of U.S. patent application Ser. No. 13/574,484, filed
Jul. 20, 2012, which is a national stage application of
International Application No. PCT/US2011/022271, filed on Jan. 24,
2011, which claims priority to U.S. Provisional Patent Application
No. 61/297,588, filed Jan. 22, 2010, the contents of all of which
are incorporated herein by reference in their entirety.
[0002] The present application is related to U.S. Pat. No.
7,102,758 titled "FOURIER DOMAIN LOW-COHERENCE INTERFEROMETRY FOR
LIGHT SCATTERING SPECTROSCOPY APPARATUS AND METHOD," which is
incorporated herein by reference in its entirety.
[0003] The present application is also related to U.S. patent
Reissue application Ser. No. 12/205,248 titled "FOURIER DOMAIN
LOW-COHERENCE INTERFEROMETRY FOR LIGHT SCATTERING SPECTROSCOPY
APPARATUS AND METHOD," which is incorporated herein by reference in
its entirety.
[0004] The present application is also related to U.S. Pat. No.
7,595,889 titled "SYSTEMS AND METHODS FOR ENDOSCOPIC ANGLE-RESOLVED
LOW COHERENCE INTERFEROMETRY," which is incorporated herein by
reference in its entirety.
[0005] The present application is also related to U.S. patent
application Ser. No. 12/538,309 titled "SYSTEMS AND METHODS FOR
ENDOSCOPIC ANGLE-RESOLVED LOW COHERENCE INTERFEROMETRY," which is
incorporated herein by reference in its entirety.
[0006] The present application is also related to U.S. patent
application Ser. No. 12/210,620 titled "APPARATUSES, SYSTEMS AND
METHODS FOR LOW-COHERENCE INTERFEROMETRY (LCI)," which is
incorporated herein by reference in its entirety.
[0007] The present application is also related to U.S. patent
application Ser. No. 11/780,879 titled "PROTECTIVE PROBE TIP,
PARTICULARLY FOR USE ON FIBER-OPTIC PROBE USED IN AN ENDOSCOPIC
APPLICATION," which is incorporated herein by reference in its
entirety.
[0008] The present application is also related to U.S. patent
application Ser. No. 12/350,689 titled "SYSTEMS AND METHODS FOR
TISSUE EXAMINATION, DIAGNOSTIC, TREATMENT AND/OR MONITORING," which
is incorporated herein by reference in its entirety.
APPENDIX
[0009] The Appendix attached hereto the present application lists
references that are referenced in this application by corresponding
number in the Appendix as indicated by brackets [ ].
BACKGROUND
[0011] 1. Field of the Disclosure
[0012] The technology of the disclosure relates to apparatuses and
methods for obtaining depth-resolved spectra using Optical
Coherence Tomography (OCT) systems and methods, as well as Fourier
domain low-coherence interferometry (f/LCI) and Fourier domain
angle-resolved low coherence interferometry (fa/LCI) systems and
methods.
[0013] 2. Technical Background
[0014] Accurately measuring small objects or other physical
phenomena is a goal that is pursued in many diverse fields of
scientific endeavor. For example, in the study of cellular biology
and cellular structures, examining the structural features of cells
is essential for many clinical and laboratory studies. The most
common tool used in the examination for the study of cells has been
the microscope. Although microscope examination has led to great
advances in understanding cells and their structure, it is
inherently limited by the artifacts of preparation. The
characteristics of the cells can only been seen at one moment in
time with their structure features altered because of the addition
of chemicals. Further, invasion is necessary to obtain the cell
sample for examination.
[0015] Thus, light scattering spectrography (LSS) was developed to
allow for in vivo examination applications, including cells. The
LSS technique examines variations in the elastic scattering
properties of cell organelles to infer their sizes and other
dimensional information. In order to measure cellular features in
tissues and other cellular structures, it is necessary to
distinguish the singly scattered light from diffuse light, which
has been multiply scattered and no longer carries easily accessible
information about the scattering objects. This distinction or
differentiation can be accomplished in several ways, such as the
application of a polarization grating, by restricting or limiting
studies and analysis to weakly scattering samples, or by using
modeling to remove the diffuse component(s).
[0016] LSS has received much attention recently as a means for
probing cellular morphology and the diagnosing of dysplasia. The
disclosures of the following references are incorporated by
reference in their entirety: Backman, V., V. Gopal, M. Kalashnikov,
K. Badizadegan, R. Gurjar, A. Wax, I. Georgakoudi, M. Mueller, C.
W. Boone, R. R. Dasari, and M. S. Feld, IEEE J. Sel. Top. Quantum
Electron., 7(6): p. 887 893 (2001); Mourant, J. R., M. Canpolat, C.
Brocker, O. Esponda-Ramos, T. M. Johnson, A. Matanock, K. Stetter,
and J. P. Freyer, J. Biomed. Opt., 5(2): p. 131 137 (2000); Wax,
A., C. Yang, V. Backman, K. Badizadegan, C. W. Boone, R. R. Dasari,
and M. S. Feld, Biophysical Journal, 82: p. 2256 2264 (2002);
Georgakoudi, I., E. E. Sheets, M. G. Muller, V. Backman, C. P.
Crum, K. Badizadegan, R. R. Dasari, and M. S. Feld, Am J Obstet
Gynecol, 186: p. 374 382 (2002); Backman, V., M. B. Wallace, L. T.
Perelman, J. T. Arendt, R. Gurjar, M. G. Muller, Q. Zhang, G.
Zonios, E. Kline, T. McGillican, S. Shapshay, T. Valdez, K.
Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S.
Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S.
Feld, Nature, 406(6791): p. 35 36 (2000); Wax, A., C. Yang, M.
Mueller, R. Nines, C. W. Boone, V. E. Steele, G. D. Stoner, R. R.
Dasari, and M. S. Feld, Cancer Res, (accepted for publication).
[0017] As an alternative approach for selectively detecting singly
scattered light from sub-surface sites, low-coherence
interferometry (LCI) has also been explored as a method of LSS. LCI
utilizes a light source with low temporal coherence, such as
broadband white light source for example. Interference is only
achieved when the path length delays of the interferometer are
matched with the coherence time of the light source. The axial
resolution of the system is determined by the coherent length of
the light source and is typically in the micrometer range suitable
for the examination of tissue samples. Experimental results have
shown that using a broadband light source and its second harmonic
allows the recovery of information about elastic scattering using
LCI. LCI has used time depth scans by moving the sample with
respect to a reference arm directing the light source onto the
sample to receive scattering information from a particular point on
the sample. Thus, scan times were on the order of 5-30 minutes in
order to completely scan the sample.
[0018] More recently, angle-resolved LCI (a/LCI) has demonstrated
the capability of obtaining structural information by examining the
angular distribution of scattered light from the sample or object
under examination. The a/LCI technique has been successfully
applied to measuring cellular morphology and to diagnosing
intraepithelial neoplasia in an animal model of carcinogenesis.
a/LCI is another means to obtain sub-surface structural information
regarding the size of a cell. Light is split into a reference and
sample beam, wherein the sample beam is projected onto the sample
at different angles to examine the angular distribution of
scattered light. The a/LCI technique combines the ability of (LCI)
to detect singly scattered light from sub-surface sites with the
capability of light scattering methods to obtain structural
information with sub-wavelength precision and accuracy to construct
depth-resolved tomographic images. Structural information is
determined by examining the angular distribution of the
back-scattered light using a single broadband light source is mixed
with a reference field with an angle of propagation. The size
distribution of the cell is determined by comparing the osciallary
part of the measured angular distributions to predictions of Mie
theory. Such a system is described in Cellular Organization and
Substructure Measured Using Angle-Resolved Low-Coherence
Inteferometry, Biophysical Journal, 82, April 2002, 2256-2265,
incorporated herein by reference in its entirety.
[0019] The a/LCI technique has been successfully applied to
measuring cellular morphology and to diagnosing intraepithelial
neoplasia in an animal model of carcinogenesis. Such a system is
described in Determining nuclear morphology using an improved
angle-resolved low coherence interferometry system in Optics
Express, 2003, 11(25): p. 3473-3484, incorporated herein by
reference in its entirety. The a/LCI method of obtaining structural
information about a sample has been successfully applied to
measuring cellular morphology in tissues and in vitro as well as
diagnosing intraepithelial neoplasia and assessing the efficacy of
chemopreventive agents in an animal model of carcinogenesis. a/LCI
has been used to prospectively grade tissue samples without tissue
processing, demonstrating the potential of the technique as a
biomedical diagnostic.
[0020] Another technique is optical coherence tomography (OCT). OCT
has been established as an excellent technique for cross-sectional
imaging of biological samples with high resolution, speed, and
sensitivity [1]. In recent years, several specialized extensions of
OCT have been developed in order to gain functional information
about probed samples [2-5]. One such extension, which seeks to
analyze depth-resolved spectroscopic information about experimental
samples, is known as spectroscopic OCT (SOCT) when applied as an
imaging technique [2, 6] and Fourier domain low coherence
interferometry (fLCI) when applied as an analysis method [7, 8].
Because the spectral scattering and absorption properties of an
experimental sample vary depending on its molecular makeup, SOCT
obtains increased contrast and functional information by spatially
mapping spectral characteristics onto coherence gated images.
[0021] In order to generate depth resolved spectroscopic
information from data collected in a single domain, SOCT typically
employs a short time Fourier transform (STFT) or a continuous
wavelet transform (CWT). The resulting depth-wavelength
distributions are analogous to time-frequency distributions (TFDs)
which have been analyzed extensively in the signal processing
literature [9, 10], but only recently analyzed in the context of
SOCT [11, 12]. Graf and Wax used the Wigner TFD from Cohen's class
of functions [13] to show that temporal coherence information
contained in the Wigner TFD cross-terms can be utilized to gain
structural knowledge of samples via SOCT signals [12]. However,
TFDs generated by the STFT are severely limited by the relationship
between time and frequency which results in an inherent tradeoff
between time (depth) resolution and frequency (wavelength)
resolution.
[0022] Work in the fields of signal processing and quantum physics
have paved the way for a new SOCT processing technique that
ameliorates the detrimental effects of the time-frequency
resolution tradeoff. Thomson, for example, developed a method
particularly well suited for stationary Gaussian signals using
orthogonal windows as means for estimating weighted averages for
spectral approximations to achieve high-resolution spectral
information [9]. Later, Bayram and Baraniuk expanded on Thomson's
method by implementing two Hermite-function-based windows to
provide a robust analysis of the time-varying spectrum of
non-stationary signals, which are pertinent to fields such as
radar, sonar, acoustics, biology, and geophysics [10]. More
recently, Lee et al [14] showed that using multiple windows
simultaneously can avoid a similar resolution tradeoff in
measurement of the position and momentum of a light field.
[0023] Current methods for analysis of spectroscopic optical
coherence tomography (SOCT) signals suffer from an inherent
tradeoff between time (depth) and frequency (wavelength)
resolution. As higher frequency resolution is sought, there is a
concomitant loss of depth resolution.
SUMMARY OF THE DETAILED DESCRIPTION
[0024] Embodiments disclosed in the detailed description include
multiple window (MW) methods and apparatuses for reconstructing
time-frequency distributions (TFDs) that apply two or more windows
(e.g., orthogonal Gaussian) can be used to independently determine
the information, including spectral information, and temporal
resolution such that it is possible to simultaneously obtain high
resolution information within a sample. In one embodiment, the MW
technique involves dual windows (DW). For example, in one
embodiment, the information may include high resolution spectral
information and temporal depth resolution information. The
disclosed MW and DW techniques can yield TFDs that contain
localized reconstructed fields without the loss of resolution, such
as spectral or temporal resolution.
[0025] In one embodiment, a method of obtaining depth-resolved
spectra of a sample for determining scattering and absorption
characteristics within the sample is provided. The method comprises
emitting a beam onto a splitter, wherein the splitter splits light
from the beam to produce a reference beam, and an input beam to the
sample. The method also comprises cross-correlating the reference
beam with a sample beam returned from the sample as a result of the
input beam by mixing the reference beam and the returned sample
beam from the sample to yield a cross-correlated profile having
optical, depth-resolved information about the returned sample beam.
The method also comprises generating a spectroscopic depth-resolved
profile that includes optical properties about the sample by:
providing first one or more spectroscopic windows of the
cross-correlated profile, each of the first one or more
spectroscopic windows having a first width at a given center
wavelength to obtain optical information about the sample for each
given center wavelength; applying a Fourier transform to the
optical information about the sample as a function of wavelength to
recover high resolution optical information about the sample at
each given center wavelength simultaneously; providing second one
or more spectroscopic windows of the cross-correlated profile, each
of the second one or more spectroscopic windows having a second
width greater than the first width at a given center wavelength to
obtain absorption information about the sample for each given
center wavelength; applying a Fourier transform to the absorption
information about the sample as a function of depth to recover high
resolution depth information about the sample at each given center
wavelength simultaneously; and co-registering the high resolution
optical information and the high resolution depth information about
the sample to yield a single high resolution spectroscopic
optical-resolved, depth-resolved profile about the sample.
[0026] In another embodiment, an apparatus for obtaining
depth-resolved information of a sample in order to determine the
scattering and absorption characteristics within the sample is
provided. The apparatus comprises a receiver adapted to receive a
reference beam and a returned sample beam containing light returned
from a sample in response to the sample receiving a sample beam,
wherein the receiver is further adapted to cross-correlate the
reference beam with the returned sample beam. The apparatus also
comprises a detector adapted to detect the cross-correlated
reference beam and the returned sample beam to yield a
cross-correlated profile having depth-resolved information about
the returned sample beam. The apparatus also comprises a processor
unit. The processor unit is adapted to generate a spectroscopic
depth-resolved profile about the sample that includes optical
properties by: providing first one or more spectroscopic windows of
the cross-correlated profile, each of the first one or more
spectroscopic windows having a first width at a given center
wavelength to obtain optical information about the sample for each
given center wavelength; applying a Fourier transform to the
optical information about the sample as a function of wavelength to
recover high resolution optical information about the sample at
each given center wavelength simultaneously; providing second one
or more spectroscopic windows of the cross-correlated profile, each
of the second one or more spectroscopic windows having a second
width greater than the first width at a given center wavelength to
obtain absorption information about the sample for each given
center wavelength; applying a Fourier transform to the absorption
information about the sample as a function of depth to recover high
resolution depth information about the sample at each given center
wavelength simultaneously; and co-registering the high resolution
optical information and the high resolution depth information about
the sample to yield a single high resolution spectroscopic
optical-resolved, depth-resolved profile about the sample.
[0027] The effectiveness of the dual window apparatuses and methods
are demonstrated in simulations and in processing of measured
Optical Coherence Tomography (OCT) signals that contain fields
which vary in time and frequency. The exemplary DW techniques
described herein can yield TFDs that maintain high spectral and
temporal resolution and are free or substantially free from the
artifacts and limitations commonly observed with other processing
methods. The DW technique can be applied to detect modulation of
OCT signals due to scattering or absorption; thus posing a
well-conditioned problem for the DW technique. The exemplary dual
window techniques described herein allow the reconstruction of the
Wigner TFD of an SOCT signal using two orthogonal windows which
independently determine spectral and temporal resolution, avoiding
the time-frequency resolution tradeoff that limits current SOCT
signal processing. Simulations and experimental results from
scattering and absorption phantoms are presented to justify the
capabilities of the approach.
[0028] Additional features and advantages will be set forth in the
detailed description which follows, and in part will be readily
apparent to those skilled in the art from that description or
recognized by practicing the embodiments as described herein,
including the detailed description that follows, the claims, as
well as the appended drawings.
[0029] It is to be understood that both the foregoing general
description and the following detailed description present
embodiments, and are intended to provide an overview or framework
for understanding the nature and character of the disclosure. The
accompanying drawings are included to provide a further
understanding, and are incorporated into and constitute a part of
this specification. The drawings illustrate various embodiments,
and together with the description serve to explain the principles
and operation of the concepts disclosed.
BRIEF DESCRIPTION OF THE FIGURES
[0030] FIG. 1A is a diagram of an exemplary embodiment of an fLCI
system;
[0031] FIG. 1B is a diagram of another exemplary embodiment of an
fLCI system using fiber optic coupling;
[0032] FIGS. 2A and 2B are diagrams illustrating exemplary
properties of a white light source;
[0033] FIGS. 3A and 3B are diagrams illustrating an exemplary axial
spatial cross-correlation function for a coverslip sample;
[0034] FIGS. 4A and 4B are diagrams of exemplary spectra obtained
for front and back surfaces, respectively, of a coverglass sample
when no microspheres are present;
[0035] FIGS. 4C and 4D are diagrams of exemplary spectra obtained
for front and back surfaces, respectively, of a coverglass sample
when microspheres are present;
[0036] FIG. 5A illustrates diagrams of exemplary spectra obtained
from a sample with first narrower windows applied to the
interference term before performing the Fourier transform operation
to obtain higher resolution spectral information about the sample,
and second wider windows separately applied to the interference
term before performing the Fourier transform operation to obtain
higher resolution depth information about the sample;
[0037] FIG. 5B illustrates diagrams of exemplary higher resolution
depth-resolved spectral information profiles including higher
resolution spectral information and higher resolution depth
information, respectively, about the sample as a function of wave
number and depth after performing Fourier transforms separately
using two different sized windows to interference terms in FIG.
5A;
[0038] FIG. 5C is an exemplary diagram of combined higher
resolution spectral and depth information depth-resolved spectral
information profiles in FIG. 5B combined together to provide a
single depth-resolved spectral information profile regarding the
sample that includes higher resolution spectral and depth
information;
[0039] FIGS. 6A and 6B are diagrams of exemplary ratios of spectra
in FIGS. 4A through 5C illustrating scattering efficiency of
spheres for front and back surface reflections;
[0040] FIG. 7 is a diagram of a generalized version of the system
shown in FIGS. 1A and 1B;
[0041] FIG. 8A is a schematic of one exemplary embodiment of the
fa/LCI system employing Mach-Zehnder interferometer;
[0042] FIG. 8B is an illustration showing the relationship of the
detected scattering angle to slit of spectrograph in the
interferometer arrangement of FIG. 8A;
[0043] FIG. 9 is a flowchart illustrating the steps performed by an
interferometer apparatus to recover depth-resolved spatial
cross-correlated information about the sample for analysis;
[0044] FIGS. 10A-D illustrate examples of fa/LCI data recovered in
the spectral domain for an exemplary sample of polystyrene beads,
comprising the total acquired signal (FIG. 10A), the reference
field intensity (FIG. 10B), the signal field intensity (FIG. 10C),
and the extracted, cross-correlated signal between the reference
and signal field intensities (FIG. 10D);
[0045] FIG. 11A is an illustration of the axial spatial
cross-correlated function performed on the cross-correlated fa/LCI
data illustrated in FIG. 10D as a function of depth and angle;
[0046] FIG. 11B is an illustration of an angular distribution plot
of raw and filtered data regarding scattered sample signal
intensity as a function of angle in order to recover size
information about the sample;
[0047] FIG. 12A is an illustration of the filtered angular
distribution of the scattered sample signal intensity compared to
the best fit Mie theory to determine size information about the
sample;
[0048] FIG. 12B is a Chi-squired minimization of size information
about the sample to estimate the diameter of cells in the
sample;
[0049] FIG. 13 is a schematic of an exemplary embodiment of the
fa/LCI system employing an optical fiber probe;
[0050] FIG. 14A is a cutaway view of an a/LCI fiber-optic probe tip
that may be employed by the fa/LCI system illustrated in FIGS. 6A
and 6B;
[0051] FIG. 14B illustrates the location of the fiber probe in the
fa/LCI system illustrated in FIG. 14A;
[0052] FIG. 15A is an illustration of an alternative fiber-optic
fa/LCI system that may be employed with the embodiments described
herein;
[0053] FIG. 15B is an illustration of sample illumination and
scattered light collection with distal end of probe in the fa/LCI
system illustrated in FIG. 15B;
[0054] FIG. 15C is an illustration of an image of the illuminated
distal end of probe of the fa/LCI system illustrated in FIG.
15A;
[0055] FIG. 16A shows an exemplary ideal time-frequency
distribution (TFD) with E.sub.1 centered at z.sub.0=5 and
k.sub.1=13 and E.sub.2 centered at z.sub.0=0 and k.sub.2=26 in a
first simulation;
[0056] FIG. 16B shows an exemplary Wigner TFD in the first
simulation;
[0057] FIG. 16C shows an exemplary MH TFD in the first
simulation;
[0058] FIG. 16D shows the exemplary TFD generated using the Dual
Window method in the first simulation;
[0059] FIG. 17A shows an exemplary ideal TFD with simulated source
bandwidth of .DELTA.k=35 length.sup.-1 units in a second simulation
modeling a SOCT signal from a Michelson interferometer;
[0060] FIG. 17B shows an exemplary TFD generated by a narrow
spectral window STFT with standard deviation=2 length.sup.-1 units
in the second simulation;
[0061] FIG. 17C shows an exemplary TFTD generated by a wide
spectral window STFT with standard deviation=45 length.sup.-1 units
in the second simulation;
[0062] FIG. 17D shows an exemplary TFD generated by using the
double window method which computes the product of the TFDs shown
in FIGS. 17B and 17C;
[0063] FIG. 17E shows exemplary time marginals (depth profile)
computed from FIGS. 17A, 17B, and 17D;
[0064] FIG. 17F shows an exemplary spectral profile of the rear
surface reflection in FIGS. 17B-17D illustrating that the DW
technique maintains higher spectral fidelity;
[0065] FIG. 18A shows an exemplary TFD of simulation 2 generated by
the dual window (DW) processing method;
[0066] FIG. 18B shows an exemplary spectral profile from the front
reflecting surface of the sample shown in FIG. 18A;
[0067] FIG. 18C shows an exemplary correlation plot with peak
corresponding to sample spacing distance of 1.5 units;
[0068] FIG. 19A is an illustration of an exemplary absorption
phantom constructed of a glass wedge filled with an absorbing
dye;
[0069] FIG. 19B shows an exemplary parallel frequency domain OCT
(pfdOCT) image of the absorption phantom with the two inner glass
surfaces clearly visible;
[0070] FIG. 19C shows an exemplary transmission spectrum of
absorbing dye used in absorption phantom which shows strong
absorption in the high wavenumber range of the detected
spectrum;
[0071] FIG. 20A illustrates an exemplary TFD of the absorption
phantom generated by a narrow spectral window STFT;
[0072] FIG. 20B illustrates an exemplary TFD of the absorption
phantom generated by a wide spectral window STFT;
[0073] FIG. 20C illustrates an exemplary TFD of the absorption
phantom generated by a moderate spectral window STFT;
[0074] FIG. 20D illustrates an exemplary TFD of the absorption
phantom generated by the dual window technique;
[0075] FIG. 21A displays exemplary spectral profiles from depths
corresponding to the absorption phantom's rear surface in the TFDs
of FIGS. 20C and 20D;
[0076] FIG. 21B shows exemplary spectral cross-sections from depths
corresponding to the absorption phantom's front surface, along with
the source's reflectance spectrum for reference;
[0077] FIG. 21C displays an exemplary time marginals for each TFD
from FIGS. 20C and 20D, along with the corresponding A-scan from
FIG. 19B;
[0078] FIG. 22A shows exemplary spectral profiles of FIG. 21A with
high frequency modulations removed;
[0079] FIG. 22B shows exemplary spectral profiles of FIG. 21B with
high frequency modulations removed;
[0080] FIG. 23A illustrates an exemplary absorption phantom TFD
generated with the DW technique;
[0081] FIG. 23B shows an exemplary spectral profile from the front
surface of the absorption phantom corresponding to the dashed line
in FIG. 23A;
[0082] FIG. 23C shows an exemplary correlation plot with peak
corresponding to phantom spacing distance that is in good agreement
with the OCT thickness measurement;
[0083] FIG. 24A shows an exemplary TFD from hamster cheek pouch
tissue generated with the DW technique;
[0084] FIG. 24B shows an exemplary average spectrum from a 15 .mu.m
depth segment corresponding to the basal tissue layer;
[0085] FIG. 24C shows an exemplary correlation plot with peak
corresponding to scatterer diameter of 4.94 .mu.m;
[0086] FIGS. 25A and 25B show an exemplary OCT image of a phantom
acquired by a single 0.3 second exposure with no scanning;
[0087] FIG. 26A shows an exemplary processed TFD of the image in
FIGS. 25A and 25B using the DW technique;
[0088] FIG. 26B shows an exemplary corresponding A-scan to the TFD
of FIG. 26A;
[0089] FIGS. 27A and 27B show exemplary spectral profiles of two
points from the A-scan of FIG. 26B;
[0090] FIGS. 27C and 27D show exemplary correlation plots for the
two points from the A-scan of FIG. 26B;
[0091] FIG. 28 shows an exemplary schematic of an exemplary pfdOCT
system;
[0092] FIG. 29A shows exemplary cell nuclei with incident and
scattered fields indicated;
[0093] FIG. 29B shows exemplary interference spectra with
wavenumber dependent oscillations caused by interference between
front and back surface reflections;
[0094] FIG. 30A shows exemplary raw data from the complete animal
trial with spectra from three spectrometer channels shown;
[0095] FIG. 30B shows three exemplary typical depth-resolved
spectroscopic plots produced by DW processing the spectra in FIG.
30A and summing the plots from all 120 channels produces the final
TFD as shown;
[0096] FIG. 31A shows an exemplary histopathology image and
corresponding depth plot for untreated epithelium;
[0097] FIG. 31B shows an exemplary histopathology image and
corresponding depth plot for treated epithelium;
[0098] FIG. 32A illustrates an exemplary depth-resolved
spectroscopic plot with basal layer indicated by dashed box;
[0099] FIG. 32B shows an exemplary spectrum from basal tissue layer
along with power law fit;
[0100] FIG. 32C shows an exemplary residual spectrum from the basal
tissue layer;
[0101] FIG. 32D shows an exemplary correlation plot generated by
Fourier transforming the spectrum in FIG. 32C, where the peak
correlation distance can be related directly to scatterer size;
[0102] FIG. 33 shows exemplary nuclear diameter measurements for
each sample of the complete animal trial;
[0103] FIG. 34 is a picture of an exemplary stained tissue sample,
four (4) weeks post treatment with three (3) aberrant crypt foci
(ACF) containing 2, 3, and 4 aberrant crypts;
[0104] FIG. 35 illustrates an exemplary parallel frequency domain
OCT system operating in scatter mode;
[0105] FIG. 36 illustrates an exemplary pfdOCT image of an ex-vivo
rat colon sample;
[0106] FIGS. 37A-37C illustrate exemplary average spectrum from the
delineated region in FIG. 36, along with a low frequency component
(black dotted line); the low frequency component is subtracted from
the averaged spectrum of obtain the local oscillations (FIG. 37A);
a Fourier transform yields a correlation function (FIG. 37B); and
the peak corresponds to an average cell nuclear diameter in the
region of analysis (FIG. 37C);
[0107] FIGS. 38A-38C illustrate exemplary nuclear diameter by depth
sections, with a mid section (e.g., 35 .mu.m in depth) providing
the most significant results, with p-values<10-4** for the
treated samples at all time points when compared to the control
group;
[0108] FIG. 39 is a table containing exemplary measured cell
nuclear diameters by depth sections (measurements in .mu.m;
p-values<10-4 **; p-values<0.05 *; N=10);
[0109] FIG. 40 is a table containing exemplary measured cell
nuclear diameters (fLCI measurement) and number of ACF by length
segments;
[0110] FIGS. 41A-41C illustrate exemplary results by colon length
segments; highly statistical differences (p-value<10-4 **) were
observed between the control group and treated groups for the
proximal left colon (LC) (FIG. 41A) and distal LC (FIG. 41B); and
FIG. 41C) plots the measured cell nuclear diameter as a function of
the number of ACF; for clarity, the time of measurement is noted
next to each point (wk=week); and
[0111] FIG. 42 is a schematic diagram representation of an
exemplary machine in the exemplary form of an exemplary computer
system adapted to execute instructions from an exemplary
computer-readable medium to perform the DW techniques described
herein.
[0112] FIG. 43 is a diagram of an exemplary embodiment of an
imaging system and schematic of a processing method for true color
spectroscopic optical coherence tomography (SOCT).
[0113] FIG. 44 illustrates tomographic images with endogenous
contrast, including a conventional OCT image (top) and a true color
SOCT image (bottom); all scale bars are 100 .mu.m.
[0114] FIGS. 45(a)-(e) illustrate an en-face image using endogenous
contrast and corresponding spectral profiles with arrows indicating
points where the spectra are quantified; all scale bars are 100
.mu.m.
DETAILED DESCRIPTION
[0115] Reference will now be made in detail to the embodiments,
examples of which are illustrated in the accompanying drawings, in
which some, but not all embodiments are shown. Indeed, the concepts
may be embodied in many different forms and should not be construed
as limiting herein; rather, these embodiments are provided so that
this disclosure will satisfy applicable legal requirements.
Whenever possible, like reference numbers will be used to refer to
like components or parts.
[0116] Embodiments disclosed in the detailed description include
multiple window (MW) methods and apparatuses for reconstructing
time-frequency distributions (TFDs) that apply two or more windows
(e.g., orthogonal Gaussian) can be used to independently determine
the information, including spectral information, and temporal
resolution such that it is possible to simultaneously obtain high
resolution information within a sample. In one embodiment, the MW
technique involves dual windows (DW). For example, in one
embodiment, the information may include high resolution spectral
information and temporal depth resolution information. The
disclosed MW and DW techniques can yield TFDs that contain
localized reconstructed fields without the loss of resolution, such
as spectral or temporal resolution.
[0117] In one embodiment, a method of obtaining depth-resolved
spectra of a sample for determining scattering and absorption
characteristics within the sample is provided. The method comprises
emitting a beam onto a splitter, wherein the splitter splits light
from the beam to produce a reference beam, and an input beam to the
sample. The method also comprises cross-correlating the reference
beam with a sample beam returned from the sample as a result of the
input beam by mixing the reference beam and the returned sample
beam from the sample to yield a cross-correlated profile having
optical, depth-resolved information about the returned sample beam.
The method also comprises generating a spectroscopic depth-resolved
profile that includes optical properties about the sample by:
providing first one or more spectroscopic windows of the
cross-correlated profile, each of the first one or more
spectroscopic windows having a first width at a given center
wavelength to obtain optical information about the sample for each
given center wavelength; applying a Fourier transform to the
optical information about the sample as a function of wavelength to
recover high resolution optical information about the sample at
each given center wavelength simultaneously; providing second one
or more spectroscopic windows of the cross-correlated profile, each
of the second one or more spectroscopic windows having a second
width greater than the first width at a given center wavelength to
obtain absorption information about the sample for each given
center wavelength; applying a Fourier transform to the absorption
information about the sample as a function of depth to recover high
resolution depth information about the sample at each given center
wavelength simultaneously; and co-registering the high resolution
optical information and the high resolution depth information about
the sample to yield a single high resolution spectroscopic
optical-resolved, depth-resolved profile about the sample.
[0118] The dual window apparatuses and methods were designed in one
embodiment to be used to recover simultaneous spectral and depth
information from a broadband OCT or LCI signal. This approach could
also be applicable to detecting multispectral information for
angle-resolved low coherence interferometry (a/LCI). In a/LCI,
scattered light is detected as a function of angle to determine the
structure of scattering objects. As an example, an a/LCI light
source may have a bandwidth of 20-40 nm to enable cellular scale
depth resolution (30 microns). However, if a light source with a
broader bandwidth were used, the dual window apparatuses and
methods could be applied to provide simultaneous depth and spectral
information in addition to the angle-resolved scattering. The
combination of scattering data at a multitude of wavelengths and
scattering angles could enable more precise data analysis and lead
to improved determinations of structural information. In this
scheme, multiple broadband sources could be used or a single source
with a large bandwidth. The key determinant here is that there is
spectrally resolved data is available. While time domain a/LCI can
be Fourier transformed to yield spectral data, the frequency domain
data acquisition modalities naturally lend themselves to this type
of analysis. Specifically, Fourier domain a/LCI, where spectral
data are acquired with a spectrometer, and swept source a/LCI,
where data are acquired by sweeping the frequency of a narrowband
laser in time, are both well suited for implementation of
multispectral a/LCI using the dual window approach.
[0119] Before discussing the exemplary DW techniques, exemplary
systems that may be employed to capture depth-resolved spectral
information regarding a sample using LCI that may then use the
exemplary DW techniques described herein to obtain high resolution
depth-resolved spectral information about the sample are first
discussed below. For example, the DW techniques described herein
may also be used in f/LCI systems. Below is a description of one
embodiment of an f/LCI system.
Exemplary f/LCI System
[0120] The contents of the following references are incorporated by
reference in their entirety: Wojtkowski, M., A. Kowalczyk, R.
Leitgeb, and A. F. Fercher, Opt. Lett., 27(16): p. 1415 1417
(2002); Wojtkowski, M., R. Leitgeb, A. Kowalczyk, T. Bajraszewski,
and A. F. Fercher, J. Biomed. Opt., 7(3): p. 457 463 (2002);
Leitgeb, R., M. Wojtkowski, A. Kowalczyk, C. K. Hitzenberger, M.
Sticker, and A. F. Fercher, Opt. Lett., 25(11): p. 820 822
(2000).
[0121] In general, spectral radar makes use of techniques where
depth-resolved structural information is recovered by applying a
Fourier transform to the spectrum of two mixed fields. In fLCI, the
aforementioned approach used in spectral radar applications is
extended to recover not only depth-resolved structure, but also to
obtain spectroscopic information about scattered light as a
function of depth. The capabilities of fLCI enable extracting the
size of polystyrene beads in a sub-surface layer based on their
light scattering spectrum. The apparatus and method according to
exemplary embodiments described herein can be applied to many
different areas. One such area of application is to recover nuclear
morphology of sub-surface cell layers.
[0122] One exemplary embodiment of the fLCI scheme is shown in FIG.
1A. White light from a Tungsten light source 100 (e.g. 6.5 W, Ocean
Optics.TM.) is coupled into a multimode fiber 101 (e.g. 200 .mu.m
core diameter). The output of the fiber 101 is collimated by an
achromatic lens 102 to produce a beam 104 (e.g. a pencil beam 5 mm
in diameter). The beam 104 is then forwarded to an fLCI system
10.
[0123] This illumination scheme achieves Kohler illumination in
that the fiber acts as a field stop, resulting in the proper
alignment of incident or illuminating light and thereby achieving
critical illumination of the sample. In the fLCI system 10, the
white light beam is split by the beamsplitter 106 (BS) into a
reference beam 105 and an input beam 107 to the sample 108. The
light returned by the sample 108, or optical information, is
recombined at the BS 106 with light reflected by the reference
mirror 114 (M). This optical information returned by the sample 108
may include scattering or reflectance properties or information In
one embodiment, light scattering by the sample 108 could be
recombined at the BS 106 with the light reflected by the reference
mirror 114 to generate an interference term having depth-resolved
spectral information or properties about the sample 108.
Alternatively, the light reflected by the sample 108 could be
recombined at the BS 106 with the light reflected by the reference
mirror 114 to generate an interference term having depth-resolved
optical information or properties about the sample 108. The light
returned by the sample 108 may also contain absorption information
or properties about the sample 108 in addition to scattering or
reflectance properties or information.
[0124] The reference beam 105 in conjunction with the reference
mirror 114 forms a portion of a reference arm that receives a first
reference light and outputs a second reference light. The input
beam 107 and the sample 108 form a portion of a sample arm that
receives a first sample light and outputs a second sample
light.
[0125] Those skilled in the art will appreciate that the light beam
can be split into a plurality of reference beams and input beams
(e.g. N reference beams and N input beams) without departing from
the spirit and scope of the embodiments described herein. Further,
the splitting of the beams may be accomplished with a beamsplitter
or a fiber splitter in the case of an optical fiber implementation
of an exemplary embodiment.
[0126] In the exemplary embodiment shown in FIG. 1A, the combined
beam is coupled into a multimode fiber 113 by an aspheric lens 110.
Again, other coupling mechanisms or lens types and configurations
may be used without departing from the spirit and scope of the
present application. The output of the fiber coincides with the
input slit of a miniature spectrograph 112 (e.g. USB2000, Ocean
Optics.TM.), where the light is spectrally dispersed and
detected.
[0127] The detected signal is linearly related to the intensity as
a function of wavelength I(.lamda.), which can be related to the
signal and reference fields (E.sub.s, E.sub.r) as:
<I(.lamda.)>=<|E.sub.s(.lamda.)|.sup.2>+<|E.sub.r(.lamda.-
)|.sup.2>+2Re<E.sub.s(.lamda.)E*.sub.r(.lamda.)>cos .PHI.
(1)
where .PHI. is the phase difference between the two fields and <
. . . > denotes an ensemble average.
[0128] The interference term is extracted by measuring the
intensity of the signal and reference beams independently and
subtracting them from the total intensity.
[0129] The axial spatial cross-correlation function,
.GAMMA..sub.SR(z) between the sample and reference fields is
obtained by resealing the wavelength spectrum into a wavenumber
(k=2.pi./.lamda.) spectrum then Fourier transforming:
.GAMMA..sub.SR(z)=.intg.dke.sup.ikz<E.sub.s(k)E*.sub.r(k)>cos
.PHI.. (2)
[0130] This term is labeled as an axial spatial cross-correlation
as it is related to the temporal or longitudinal coherence of the
two fields.
[0131] Another exemplary embodiment of an fLCI scheme is shown in
FIG. 1B. In this exemplary embodiment, fiber optic cable is used to
connect the various components. Those skilled in the art will
appreciate that other optical coupling mechanisms, or combinations
thereof, may be used to connect the components without departing
from the spirit and scope of the present application.
[0132] In FIG. 1B, white light from a Tungsten light source 120 is
coupled into a multimode fiber 122 and the white light beam in the
multimode fiber is split by the fiber splitter (FS) 124 into a
reference fiber 125 and a sample fiber 127 to the sample 130. The
fiber splitter 124 is used to split light from one optical fiber
source into multiple sources.
[0133] The reference light in reference fiber 125, in conjunction
with a lens 126 (preferably an aspheric lens) and the reference
mirror 128, forms a portion of a reference arm that receives a
first reference light and outputs a second reference light.
Specifically, reference light in reference fiber 125 is directed to
the reference mirror 128 by lens 126, and the reference light
reflected by the reference mirror 128 (second reference light) is
coupled back into the reference fiber 125 with lens 126. The sample
light in sample fiber 127 and the sample 130 form a portion of a
sample arm that receives a first sample light and outputs a second
sample light. Specifically, sample light in sample fiber 127 is
directed to the sample 130 by lens 131 (preferably as aspheric
lens), and at least a portion of the sample light scattered by the
sample 130 is coupled into the sample fiber 127 by lens 131. In the
exemplary embodiment shown in FIG. 1B, the sample 130 is preferably
spaced from lens 131 by a distance approximately equal to the focal
length of lens 131.
[0134] At least a portion of the reflected reference light in
reference fiber 125 and at least a portion of the scattered sample
light on sample fiber 127 are coupled into a detector fiber 133 by
the FS 124. The detector fiber 133 may be placed to collect light
scattered from the sample 130 as illustrated, or alternatively to
collect light reflected from the sample 130.
[0135] The output of detector fiber 133 coincides with the input of
a miniature spectrograph 132, where the light is spectrally
dispersed and detected.
[0136] FIGS. 2A and 2B illustrate some of the properties of a white
light source. FIG. 2A illustrates an autocorrelation function
showing a coherence length (l.sub.C=1.2 .mu.m). FIG. 2A shows the
cross-correlation between the signal and reference fields when the
sample is a mirror, and this mirror is identical to the reference
mirror (M). In this exemplary scenario, the fields are identical
and the autocorrelation is given by the transform of the incident
field spectrum, modeled as a Gaussian spectrum with center
wavenumber k.sub.o=10.3 .mu.m.sup.-1 and 1/e width
.DELTA.k.sub.1/e=2.04 .mu.m.sup.-1 (FIG. 2B).
[0137] FIG. 2B shows an exemplary spectrum of light source that can
be used in accordance with the embodiments described herein.
[0138] From this autocorrelation, the coherence length of the
field, l.sub.c=1.21 .mu.m is determined. This is slightly larger
than the calculated width of l.sub.c=2/.DELTA.k.sub.l/c=0.98 .mu.m,
with any discrepancy most likely attributed to uncompensated
dispersion effects. Note that rescaling the field into wavenumber
space is a nonlinear process which can skew the spectrum if not
properly executed [13].
[0139] In data processing, a fitting algorithm is applied (e.g. a
cubic spline fit) to the rescaled wavenumber spectrum and then
resampled (e.g. resample with even spacing). The resampled spectrum
is then Fourier transformed to yield the spatial correlation of the
sample. Those skilled in the art will appreciate that other
frequency based algorithms or combinations of algorithms can be
used in place of the Fourier transform to yield spatial
correlation. One example of a software tool that can be used to
accomplish this processing in real time or near real time is to use
LabView.TM. software.
[0140] In one exemplary embodiment, the sample consists of a glass
coverslip (e.g., thickness, d.about.200 .mu.m) with polystyrene
beads which have been dried from suspension onto the back surface
(1.55 .mu.m mean diameter, 3% variance). Thus, the field scattered
by the sample can be expressed as:
E.sub.s(k)=E.sub.front(k)e.sup.ik.sup..delta..sup.z+E.sub.back(k)e.sup.i-
k(.sup..delta..sup.z+nd) (3)
[0141] In equation 3, E.sub.front and E.sub.back denote the field
scattered by the front and back surfaces of the coverslip, and
.delta.z is the difference between the path length of the reference
beam and that of the light reflected from the front surface and n
the index of refraction of the glass. The effect of the
microspheres will appear in the E.sub.back term as the beads are
small and attached closely to the back surface. Upon substituting
equation 3 into equation 2, a two peak distribution with the width
of the peaks given by the coherence length of the source is
obtained.
[0142] In order to obtain spectroscopic information, a Gaussian
window is applied to the interference term before performing the
Fourier transform operation. Those skilled in the art will
appreciate that other probabilistic windowing methodologies may be
applied without departing from the spirit and scope of the
embodiments described herein. This makes it possible to recover
spectral information about light scattered at a particular
depth.
[0143] The windowed interference term takes the form:
<E.sub.s(k)E*.sub.r(k)>exp[-((k-k.sub.w)/.DELTA.k.sub.w).sup.2].
(4)
[0144] The proper sizing of a windowed interference term can
facilitate the processing operation. For example, by selecting a
relatively narrow window (.DELTA.k.sub.w small) compared to the
features of E.sub.s and E.sub.k, it is effectively obtained
<Es(kw)E*r(kw)>. In processing the data below,
.DELTA.k.sub.w=0.12 .mu.m.sup.-1 is used, which degrades the
coherence length by a factor of 16.7. This exemplary window setting
enables the scattering at 50 different wavenumbers over the 6
.mu.m.sup.-1 span of usable spectrum. In this example, a single
Gaussian window is applied to the interference term before
performing the Fourier transform. However, as will be discussed in
more detail below, two windows may be applied to the interference
term.
[0145] In FIGS. 3A and 3B, an axial spatial cross-correlation
function for a coverslip sample is shown according to one
embodiment. FIGS. 3A and 3B shows the depth resolved
cross-correlation profiles of the coverslip sample before and after
the processing operations. In FIG. 3A, a high resolution scan with
arrows indicating a peak corresponding to each glass surface is
shown. In FIG. 3B, a low resolution scan is obtained from the scan
in FIG. 3A is shown by using a Gaussian window.
[0146] Note that the correlation function is symmetric about z=0,
resulting in a superposed mirror image of the scan. Since these are
represented as cross-correlation functions, the plots are symmetric
about z=0. Thus the front surface reflection for z>0 is paired
with the back surface reflection for z<0, and vice versa.
[0147] In FIG. 3A, the reflection from the coverslip introduces
dispersion relative to the reflection from the reference arm,
generating multiple peaks in the profile. When the spectroscopic
window is applied, only a single peak is seen for each surface,
however several dropouts appear due to aliasing of the signal.
[0148] To obtain the spectrum of the scattered light, the Gaussian
window is repeatedly applied where the center wavenumber is
increased by 0.12 .mu.m.sup.-1 between successive applications. As
mentioned above, .DELTA.k.sub.w=0.12 .mu.m.sup.-1 is used to
degrade the coherence length by a factor of 16.7. This results in
the generation of a spectroscopic depth-resolved profile.
[0149] FIGS. 4A and 4B show the spectrum obtained for light
scattered from the front (a) and back (b) surfaces of a coverglass
sample respectively, when no microspheres are present. The
reflection from the front surface appears as a slightly modulated
version of the source spectrum. The spectrum of the reflection from
the rear surface however has been significantly modified. Thus in
equation 3, it is now taken that E.sub.front(k)=E.sub.s(k) and
E.sub.back(k)=T(k)E.sub.s(k), where T(k) represents the
transmission through the coverslip.
[0150] In FIGS. 4C and 4D, the spectra for light scattering
obtained for front (a) and back (b) surfaces of a coverglass sample
when microspheres are present on the back surface of the coverslip
are shown in FIG. 4C and FIG. 4D. It can be seen that the reflected
spectrum from the front surface has not changed significantly, as
expected. However, the spectrum for the back surface is now
modulated. One can examine the scattering properties S(k) of the
microspheres by writing the scattered field as
E.sub.spheres(k)=S(k)T(k)E.sub.s(k) and taking the ratio
E.sub.spheres(k)/E.sub.back(k)=S(k), which is shown as a solid line
in FIG. 6A. It can be seen from this ratio that the microspheres
induce a periodic modulation of the spectrum.
[0151] As will be discussed in more detail below, it also possible
to provide a multiple window (MW), for example a dual window (DW)
technique, to obtain depth-resolved spectral information. When
providing one window, as discussed above, the same window size is
provided for recovering both depth and spectral information. A
tradeoff exists when providing a single window size for sampling
the interference term. When a single window size is provided,
resolution is lost in both the spectral and depth information from
the interference term. This is because applying a wide window
provides lower resolution spectral information, but provides higher
resolution depth information due to the nature of the Fourier
transform. Applying a narrow window provides lower resolution depth
information, but provides higher resolution spectral information
due to the nature of the Fourier transform. Thus, by providing a
single window that provides a compromise between a wide and narrow
window of the interference term, resolution information is lost for
both the spectral and depth information about the sample.
[0152] To obtain depth-resolved spectroscopic information, the DW
technique is used in certain embodiments disclosed herein. In this
regard, FIG. 5A illustrates diagrams of interferograms 500, 502 of
exemplary spectra obtained from a sample with first narrower
windows 504 applied to the interference term before performing the
Fourier transform operation to obtain high resolution spectral
information about the sample, and second wider windows 506 applied
to the interference term before performing the Fourier transform
operation to obtain high resolution depth information about the
sample. The DW technique consists of multiplying two STFTs that
operate on each interferogram 502, 502. A STFT is implemented by
sweeping a window across the interferometric data while
simultaneously taking a Fourier transform at each step, thus giving
a map of the spectral content confined within a spatial (or axial)
region. These maps are known as time-frequency distributions
(TFDs). However, TFDs obtained using a single STFT suffer from an
inherent trade-off between the resulting spectral and spatial
resolutions. The DW technique, on the other hand, utilizes the high
spectral resolution of an STFT using a narrow window, and the high
spatial resolution of an STFT using a wide window to avoid the
deleterious effects of the time-frequency trade-off. Here in this
example, Gaussian windows were used with standard deviations
w1=0.029 .mu.m-1 and w2=0.804 .mu.m-1, resulting in TFDs with an
axial resolution of 3.45 .mu.m and spectral resolution of 1.66 nm.
Note that this process is conducted for each A-scan, thus giving a
spectrum for each point in an OCT image.
[0153] FIG. 5B illustrates depth-resolved spectral information
profile diagrams 508, 510 of exemplary resulting high resolution
spectral and depth information about the sample, respectively, as a
function of wave number and depth after performing a Fourier
transform to interference term in FIG. 5A. As shown in FIG. 5B,
diagram 508 provides higher resolution spectral information, but
lower resolution depth information. Diagram 510 in FIG. 5B provides
higher resolution depth information, but lower resolution spectral
information. This process involves using the images to identify the
contour of the tissue surfaces and calibrate the analysis relative
to this "zero" depth. Note that if a surface is not clearly
discernable at any particular A-scan, no further analysis is
conducted there. With this information, the DW TFDs can be properly
aligned and thus consistently provide spectral information from
specific tissue depths.
[0154] Once the spectra are properly aligned in FIG. 5B, regions of
interest, both laterally and axially, are identified and averaged
in order to provide sufficient signal-to-noise ratio for the
spectral analysis that follows. In the lateral direction in this
example, twenty (20) DW TFDs are averaged to yield ten (10)
different lateral segments in each OCT image. Note that in previous
studies, all TFDs in an image were averaged; thus, the analysis
provided here produces a ten-fold increase of the spatial
information. In the axial direction, the spectral averages of 25
.mu.m depth segments in this example can be calculated from three
different sections: at the surface (surface section 0-25 .mu.m),
centered about 35 .mu.m in depth (mid section. 22.5-47.5 .mu.m),
and centered about 50 .mu.m in depth (low section 37.5-62.5
.mu.m).
[0155] To obtain a single depth-resolved spectral information
profile that includes both higher resolution spectral and depth
information regarding the sample, the depth-resolved spectral
information profile diagrams or OCT images 508, 510 in FIG. 5B can
be combined or co-registered, as illustrated in FIG. 5C. FIG. 5C is
an exemplary diagram 512 of combined higher resolution spectral and
depth information depth-resolved spectral information profiles 508,
510 in FIG. 5B combined together to provide a single depth-resolved
spectral information profile regarding the sample that includes
higher resolution spectral and depth information. The diagram 512
in FIG. 5C is provided by co-registering the OCT images 508, 510 in
FIG. 5B with the DW TFDs.
[0156] Providing a depth-resolved spectral information profile that
includes higher resolution spectral and depth information allows
isolation and observation of scattering properties of the sample
down to a high resolution, such as down to micrometers of depth, as
illustrated in FIG. 5C. This allows observation of absorption
features of the cells of the sample. Thus, with higher resolution
spectral properties, scattering properties as a function of color
may be identified and distinguished at depths, as opposed to a
lower resolution depth-resolved spectral information profile, where
wavelength information is mixed losing the ability to specifically
pinpoint color properties from the scattering properties of the
sample as a function of depth.
[0157] Obtaining higher resolution scattering properties allows
analysis of the scatting properties within a few micrometers, as an
example, as opposed to a larger area with scattering properties
averaged due to lower resolution information. Thus, obtaining
higher resolution scattering properties may also allow providing
accurate color information for scattering properties. For example,
hemoglobin in blood appears red in color, because hemoglobin
absorbs blue light. Thus, by providing higher resolution depth
information of scattering properties without compromising higher
resolution spectral information, hemoglobin may be accurately
identified in the depth-resolved spectral information profile of
the sample. Also, absorption of biological absorbers, may be
viewable and discernable with higher resolution depth-resolved
spectral information profile. Examples of biological absorbers
include Hemoglobin and melanin. The biological absorbers may also
include contracts agents for example, such as fluoroscene. The
present application is not limited to any particular contrast
agents.
[0158] Turning back to an example of a single window technique. in
FIG. 6A, ratios of the spectra found in FIGS. 4A and 4B, and FIGS.
4C and 4D are shown. This illustrates the scattering efficiency of
spheres for front (represented by the dashed line) and back
(represented by the solid line) surface reflections. In FIG. 6B, a
correlation function obtained from ratio of back surface
reflections is shown. The peak occurs at the round trip optical
path through individual microspheres, permitting the size of the
spheres to be determined with sub-wavelength accuracy.
[0159] For comparison, the same ratio for the front surface
reflections (dashed line in FIG. 6A) shows only a small linear
variation. Taking the Fourier transform of S(k) yields a clear
correlation peak (FIG. 6B), at a physical distance of z=5.24 .mu.m.
This can be related to the optical path length through the sphere
by z=2n1 with the index of the microspheres n=1.59. The diameter of
the microspheres to be 1=1.65 .mu.m+/-0.33 .mu.m, with the
uncertainty given by the correlation pixel size. Thus with fLCI,
one is able to determine the size of the microspheres with
sub-wavelength accuracy, even exceeding the resolution achievable
with this white light source and related art LCI imaging.
[0160] There are many applications of the various exemplary
embodiments of the present application. One exemplary application
of fLCI is in determining the size of cell organelles, in
particular the cell nucleus, in epithelial tissues. In biological
media, for example, the relative refractive indices are lower for
organelles compared to microspheres and thus, smaller scattering
signals are expected. The use of a higher power light source will
permit the smaller signals to be detected. Other examples include
detection of sub-surface defects in manufactured parts, including
fabricated integrated circuits, detection of airborne aerosols,
such as nerve agents or biotoxins, and detection of exposure to
such aerosols by examining epithelial tissues within the
respiratory tract.
[0161] Additionally, the larger the size of the nucleus (compared
to the microspheres in this experiment), the higher the frequency
modulation of the spectrum. Those skilled in the art will
appreciate that higher frequency oscillations are detected at a
lower efficiency in Fourier transform spectroscopy techniques.
Therefore, in order to detect these higher frequency oscillations,
a higher resolution spectrograph is used.
[0162] FIG. 7 illustrates a generalized embodiment of the fLCI
system shown in FIG. 1 and discussed in greater detail above. In
FIG. 7, a light source 700 (e.g. a multi-wavelength light) is
coupled into an fLCI system 702. Within the fLCI system 702, a
sample portion 704 and a reference portion 706 are located. The
sample portion 704 includes a light beam and light scattered from a
sample. For example, the sample portion 704 may include a sample
holder, a free space optical arm, or an optical fiber. The
reference portion 706 includes a light beam and light that is
reflected from a reference. For example, the reference portion 706
may include an optical mirror. A cross-correlator 708 receives and
cross-correlates light from the sample with light from the
reference.
Exemplary fa/LCI Systems
[0163] The DW technique is also applicable to a/LCI systems,
including the a/LCI technique called Fourier domain a/LCI (fa/LCI),
which enables data acquisition at rapid rates using a single scan,
sufficient to make in vivo applications feasible. Angle-resolved
and depth-resolved spectra information may be obtained about a
sample, in which depth and size information about the sample can be
obtained with a single scan, and wherein the reference arm can
remain fixed with respect to the sample due to only one scan
required. A reference signal and a scattered sample signal are
cross-correlated and dispersed at a multitude of scattered angles
off of the sample, thereby representing scatterers from a multitude
of points on the sample at the same time in parallel.
[0164] Since this angle-resolved, cross-correlated signal is
spectrally dispersed, the new data acquisition scheme is
significant as it permits data to be obtained in less than one
second, a threshold determined to be necessary for acquiring data
from in vivo tissues. Information about all depths of the sample at
each of the multitude of different points on the sample can be
obtained with one scan on the order of approximately 40
milliseconds. From the spatial, cross-correlated reference signal,
structural (size) information can also be obtained using techniques
that allow size information of scatterers to be obtained from
angle-resolved data.
[0165] The fa/LCI technique uses the Fourier domain concept to
acquire depth resolved information. Signal-to-noise and
commensurate reductions in data acquisition time are possible by
recording the depth scan in the Fourier (or spectral) domain. The
fa/LCI system combines the Fourier domain concept with the use of
an imaging spectrograph to spectrally record the angular
distribution in parallel. Thereafter, the depth-resolution is
achieved by Fourier transforming the spectrum of two mixed fields
with the angle-resolved measurements obtained by locating the
entrance slit of the imaging spectrograph in a Fourier transform
plane to the sample. This converts the spectral information into
depth-resolved information and the angular information into a
transverse spatial distribution. The capabilities of fa/LCI have
been initially demonstrated by extracting the size of polystyrene
beads in a depth-resolved measurement.
[0166] An exemplary apparatus, as well as the steps involved in the
process of obtaining angle and depth-resolved distribution data
scattered from a sample, are also set forth in FIG. 9. The fa/LCI
scheme in accordance with one embodiment is based on a modified
Mach-Zehnder interferometer as illustrated in FIG. 8A. Broadband
light 11 from a superluminescent diode (SLD) 12 is directed by a
mirror 13 (step 60 in FIG. 9) and split into a reference beam 14
and an input beam 16 to a sample 18 by beamsplitter BS1 20 (step 62
in FIG. 9). The output power of the SLD 12 may be 3 milliWatts,
having a specification of .lamda.o=850 nm, .DELTA..lamda.=20 nm
FWHM for example, providing sufficiently low coherence length to
isolate scattering from a cell layer within tissue. The path length
of the reference beam 14 is set by adjusting retroreflector RR 22,
but remains fixed during measurement. The reference beam 14 is
expanded using lenses L1 (24) and L2 (26) to create illumination
(step 64 in FIG. 9), which is uniform and collimated upon reaching
a spectrograph slit 48 in an imaging spectrograph 29. For example,
L1 may have a focal length of 1.5 centimeters, and L2 26 may have
focal length of 15 centimeters.
[0167] Lenses L3 (31) and L4 (38) are arranged to produce a
collimated pencil beam 30 incident on the sample 18 (step 66 in
FIG. 9). By displacing lens L4 (38) vertically relative to lens L3
(31), the input beam 30 is made to strike the sample at an angle of
0.10 radians relative to the optical axis. This arrangement allows
the full angular aperture of lens L4 (38) to be used to collect
scattered light 40 from the sample 18. Lens L4 (38) may have a
focal length of 3.5 centimeters.
[0168] The light 40 scattered by the sample 18 is collected by lens
L4 (32) and relayed by a 4 f imaging system comprised of lenses L5
(43) and L6 (44) such that the Fourier plane of lens L4 (32) is
reproduced in phase and amplitude at the spectrograph slit 48 (step
68 in FIG. 9). The scattered light 40 is mixed with the reference
field 14 at a second beamsplitter BS2 42 with the combined fields
46 falling upon the entrance slit (illustrated in FIG. 8B as
element 48) to the imaging spectrograph 29 (step 70 in FIG. 9). The
imaging spectrograph 29 may be the model SP2150i, manufactured by
Acton Research for example. FIG. 8B illustrates the distribution of
scattering angle across the dimension of the slit 48. The mixed
fields are dispersed with a high resolution grating (e.g. 1200
l/mm) and detected using a cooled CCD 50 (e.g. 1340.times.400, 20
.mu.m.times.20 .mu.m pixels, Spec10:400, manufactured by Princeton
Instruments) (step 72 in FIG. 9).
[0169] The detected signal 46 is a function of vertical position on
the spectrograph slit 48, y, and wavelength once the light is
dispersed by the spectrograph 29. The detected signal at pixel (m,
n) can be related to the signal 40 and reference fields 16
(E.sub.s, E.sub.r) as:
I(.lamda..sub.m,y.sub.n)=|E.sub.r(.lamda..sub.m,y.sub.n)|.sup.2+|E.sub.s-
(.lamda..sub.m,y.sub.n)|.sup.2+2ReE.sub.s(.lamda..sub.m,y.sub.n)E.sub.r*(.-
lamda..sub.m,y.sub.n) cos .phi., (5)
where .phi. is the phase difference between the two fields 30, 16
and . . . denotes an ensemble average in time. The interference
term is extracted by measuring the intensity of the signal 30 and
reference beams 16 independently and subtracting them from the
total intensity.
[0170] In order to obtain depth resolved information, the
wavelength spectrum at each scattering angle is interpolated into a
wavenumber (k=2.pi./.lamda.) spectrum and Fourier transformed to
give a spatial cross correlation, .GAMMA..sub.SR(z) for each
vertical pixel y.sub.n:
.GAMMA..sub.SR(z,y.sub.n)=.intg.dke.sup.ikzE.sub.s(k,y.sub.n)E.sub.r*(k,-
y.sub.n) cos .PHI.. (6)
The reference field 14 takes the form:
E.sub.r(k)=E.sub.oexp.left
brkt-bot.-((k-k.sub.o)/.DELTA.k).sup.2.right brkt-bot.exp.left
brkt-bot.-((y-y.sub.o)/.DELTA.y).sup.2.right
brkt-bot.exp[ik.DELTA.l] (7)
where k.sub.o (y.sub.o and .DELTA.k (.DELTA.y) represent the center
and width of the Gaussian wavevector (spatial) distribution and
.DELTA.l is the selected path length difference. The scattered
field 40 takes the form
E.sub.s(k,.theta.)=.SIGMA..sub.jE.sub.oexp[-((k-k.sub.o)/.DELTA.k).sup.2-
]exp[ikl.sub.j]S.sub.j(k,.theta.) (8)
where S.sub.j represents the amplitude distribution of the
scattering originating from the jth interface, located at depth
l.sub.j. The angular distribution of the scattered field 40 is
converted into a position distribution in the Fourier image plane
of lens L4 through the relationship y=f.sub.4 .theta.. For the
pixel size of the CCD 50 (e.g. 20 .mu.m), this yields an angular
resolution (e.g. 0.57 mrad) and an expected angular range (e.g. 228
mrad.).
[0171] Inserting Eqs. (7) and (8) into Eq. (6) and noting the
uniformity of the reference field 14 (.DELTA.y>>slit height)
yields the spatial cross correlation at the nth vertical position
on the detector 29:
.GAMMA. SR ( z , y n ) = j .intg. k E o 2 exp [ - 2 ( ( k - k o ) /
.DELTA. k ) 2 ] exp [ k ( z - .DELTA. l + l j ) ] .times. S j ( k ,
.theta. n = y n / f 4 ) cos .phi. . ( 9 ) ##EQU00001##
Evaluating this equation for a single interface yields:
.GAMMA..sub.SR(z,y.sub.n)=|E.sub.o|.sup.2exp[-((z-.DELTA.l+l.sub.j).DELT-
A.k).sup.2/8]S.sub.j(k.sub.o,.theta..sub.n=y.sub.n/f.sub.4)cos
.PHI.. (10)
[0172] Here in this example, it is assumed that the scattering
amplitude S does not vary appreciably over the bandwidth of the
source light 12. This expression shows that one can obtain a depth
resolved profile of the scattering distribution 40 with each
vertical pixel corresponding to a scattering angle.
[0173] FIG. 10A below shows typical data representing the total
detected intensity (Equation (5), above) of the sum of the
reference field 16 and the field scattered 40 by a sample of
polystyrene beads, in the frequency domain given as a function of
wavelength and angle, given with respect to the backwards
scattering direction. In an exemplary embodiment, this data was
acquired in 40 milliseconds and records data over 186 mrad,
approximately 85% of the expected range, with some loss of signal
at higher angles.
[0174] FIGS. 10B and 10C illustrate the intensity of the reference
and signal fields 14, 30 respectively. Upon subtraction of the
signal and reference fields 14, 30 from the total detected
intensity, the interference 46 between the two fields is realized
as illustrated in FIG. 10D. At each angle, interference data 46 are
interpolated into k-space and Fourier transformed to give the
angular depth resolved profiles of the sample 18 as illustrated in
FIG. 11A. The Fourier transform of the angle-resolved, cross
correlated signal 46, which is the result of signal 40 scattered at
a multitude of angles off the sample 18 and obtained in the Fourier
plane of lens L4 (32), produces depth-resolved information about
the sample 18 as a function of angle and depth. This provides
depth-resolved information about the sample 18. Because the
angle-resolved, cross-correlated signal 46 is spectrally dispersed,
the data acquisition permits data to be obtained in less than one
second. Information about all depths of the sample 18 at each of
the multitude of different points (i.e. angles) on the sample 18
can be obtained with one scan on the order of approximately 40
milliseconds. Normally, time domain based scanning is required to
obtain information about all depths of a sample at a multitude of
different points, thus requiring substantial time and movement of
the reference arm with respect to the sample.
[0175] In the experiments that produced the depth-resolved profit
of the sample 18 illustrated in FIG. 11A, the sample 18 consists of
polystyrene microspheres (e.g. n=1.59, 10.1 .mu.m mean diameter,
8.9% variance, NIST certified, Duke Scientific) suspended in a
mixture of 80% water and 20% glycerol (n=1.36) to provide neutral
buoyancy. The solution was prepared to obtain a scattering length
l=200 .mu.m. The sample is contained in a round well (8 mm
diameter, 1 mm deep) behind a glass coverslip (thickness,
d.about.170 .mu.m) (not shown). The sample beam 30 is incident on
the sample 18 through the coverslip. The round trip thickness
through the coverslip (2 n d=2 (1.5) (170 .mu.m)=0.53 mm--see FIG.
11A) shows the depth resolved capability of the approach. The data
are ensemble averaged by integrating over one mean free path (MFP).
The spatial average can enable a reduction of speckle when using
low-coherence light to probe a scattering sample. To simplify the
fitting procedure, the scattering distribution is low pass filtered
to produce a smoother curve, with the cutoff frequency chosen to
suppress spatial correlations on length scales above 16 .mu.m.
[0176] In addition to obtaining depth-resolved information about
the sample 18, the scattering distribution data (i.e. a/LCI data)
obtained from the sample 18 using the disclosed data acquisition
scheme can also be used to make a size determination of the nucleus
using the Mie theory. A scattering distribution 74 of the sample 18
is illustrated in FIG. 11B as a contour plot. The raw scattered
information 74 about the sample 18 is shown as a function of the
signal field 30 and angle. A filtered curve is determined using the
scattered data 74. Comparison of the filtered scattering
distribution curve 76 (i.e. a representation of the scattered data
74) to the prediction of Mie theory (curve 78 in FIG. 12A) enables
a size determination to be made.
[0177] In order to fit the scattered data 76 to Mie theory, the
a/LCI signals are processed to extract the oscillatory component
which is characteristic of the nucleus size. The smoothed data 76
are fit to a low-order polynomial (4.sup.th order was used for
example herein, but later studies use a lower 2.sup.nd order),
which is then subtracted from the distribution 76 to remove the
background trend. The resulting oscillatory component is then
compared to a database of theoretical predictions obtained using
Mie theory 78 from which the slowly varying features were similarly
removed for analysis.
[0178] A direct comparison between the filtered a/LCI data 76 and
Mie theory data 78 may not possible, as the chi-squared fitting
algorithm tends to match the background slope rather than the
characteristic oscillations. The calculated theoretical predictions
include a Gaussian distribution of sizes characterized by a mean
diameter (d) and standard deviation (.delta.D) as well as a
distribution of wavelengths, to accurately model the broad
bandwidth source.
[0179] The best fit (FIG. 12A) is determined by minimizing the
Chi-squared between the data 76 and Mie theory (FIG. 12B), yielding
a size of 10.2+/-1.7 .mu.m, in excellent agreement with the true
size. The measurement error is larger than the variance of the bead
size, most likely due to the limited range of angles recorded in
the measurement.
[0180] As an alternative to processing the a/LCI data and comparing
to Mie theory, there are several other approaches which could yield
diagnostic information. These include analyzing the angular data
using a Fourier transform to identify periodic oscillations
characteristic of cell nuclei. The periodic oscillations can be
correlated with nuclear size and thus will possess diagnostic
value. Another approach to analyzing a/LCI data is to compare the
data to a database of angular scattering distributions generated
with finite element method (I-EM) or T-Matrix calculations. Such
calculations may offer superior analysis as there are not subject
to the same limitations as Mie theory. For example, FEM or T-Matrix
calculations can model non-spherical scatterers and scatterers with
inclusions while Mie theory can only model homogenous spheres.
[0181] As an alternative embodiment, the systems described herein
can also employ optical fibers to deliver and collect light from
the sample of interest to use in the a/LCI system for endoscopic
applications. This alternative embodiment is illustrated in FIG.
13.
[0182] The fiber optic a/LCI scheme for this alternative embodiment
makes use of the Fourier transform properties of a lens. This
property states that when an object is placed in the front focal
plane of a lens, the image at the conjugate image plane is the
Fourier transform of that object. The Fourier transform of a
spatial distribution (object or image) is given by the distribution
of spatial frequencies, which is the representation of the image's
information content in terms of cycles per mm. In an optical image
of elastically scattered light, the wavelength retains its fixed,
original value and the spatial frequency representation is simply a
scaled version of the angular distribution of scattered light.
[0183] In the fiber optic a/LCI scheme, the angular distribution is
captured by locating the distal end of the fiber bundle in a
conjugate Fourier transform plane of the sample using a collecting
lens. This angular distribution is then conveyed to the distal end
of the fiber bundle where it is imaged using a 4 f system onto the
entrance slit of an imaging spectrograph. A beamsplitter is used to
overlap the scattered field with a reference field prior to
entering the slit so that low coherence interferometry can also be
used to obtain depth resolved measurements.
[0184] Turning now to FIG. 13, the fiber optic fa/LCI scheme is
shown. Light 12' from a broadband light source 11' is split into a
reference field 14' and a signal field 16' using a fiber splitter
(FS) 80. A splitter ratio of 20:1 is chosen in one embodiment to
direct more power to a sample 18' via the signal arm 82 as the
light returned by the tissue is typically only a small fraction of
the incident power. Alternatively, the light source 11' could be
provided by another light source, such as a super continuum laser,
or swept-source laser, as described in U.S. patent application Ser.
No. 12/210,620 titled "APPARATUSES, SYSTEMS AND METHODS FOR
LOW-COHERENCE INTERFEROMETRY (LCI)," which is incorporated herein
by reference in its entirety.
[0185] Light in the reference fiber 14' emerges from fiber F1 and
is collimated by lens L1 (84), which is mounted on a translation
stage 86 to allow gross alignment of the reference arm path length.
This path length is not scanned during operation but may be varied
during alignment. A collimated beam 88 is arranged to be equal in
dimension to the end 91 of fiber bundle F3 (90) so that the
collimated beam 88 illuminates all fibers in F3 with equal
intensity. The reference field 14' emerging from the distal tip of
F3 (90) is collimated with lens L3 (92) in order to overlap with
the scattered field conveyed by fiber F4 (94). In an alternative
embodiment, light emerging from fiber F1 (14') is collimated then
expanded using a lens system to produce a broad beam.
[0186] The scattered field is detected using a coherent fiber
bundle. The scattered field is generated using light in the signal
arm 82 which is directed toward the sample 18' of interest using
lens L2 (98). As with the free space system, lens L2 (98) is
displaced laterally from the center of single-mode fiber F2 such
that a collimated beam is produced which is traveling at an angle
relative to the optical axis The fact that the incident beam
strikes the sample at an oblique angle is essential in separating
the elastic scattering information from specular reflections. The
light scattered by the sample 18' is collected by a fiber bundle
consisting of an array of coherent single mode or multi-mode
fibers. The distal tip of the fiber is maintained one focal length
away from lens L2 (98) to image the angular distribution of
scattered light. In the embodiment shown in FIG. 13, the sample 18'
is located in the front focal plane of lens L2 (98) using a
mechanical mount 1100. In the endoscope compatible probe shown in
FIG. 14A, the sample is located in the front focal plane of lens L2
(98) using a transparent sheath (element 1102).
[0187] As illustrated in FIG. 13 and also FIG. 14B, scattered light
1104 emerging from a proximal end 1105 of the fiber probe F4 (94)
is recollimated by lens L4 (1104) and overlapped with the reference
field 14' using beamsplitter BS (1108). The two combined fields
1110 are re-imaged onto the slit (element 48' in FIG. 14) of the
imaging spectrograph 29' using lens L5 (1112). The focal length of
lens L5 (1112) may be varied to optimally fill the slit 48'. The
resulting optical signal contains information on each scattering
angle across the vertical dimension of the slit 48' as described
above for the apparatus of FIGS. 8A and 8B.
[0188] It is expected that the above-described a/LCI fiber-optic
probe will collect the angular distribution over a 0.45 radian
range (approx. 30 degrees) and will acquire the complete depth
resolved scattering distribution 1110 in a fraction of a
second.
[0189] There are several possible schemes for creating the fiber
probe which are the same from an optical engineering point of view.
One possible implementation would be a linear array of single mode
fibers in both the signal and reference arms. Alternatively, the
reference arm 96 could be composed of an individual single mode
fiber with the signal arm 82 consisting of either a coherent fiber
bundle or linear fiber array.
[0190] The fiber probe tip can also have several implementations
which are substantially equivalent. These would include the use of
a drum or ball lens in place of lens L2 (98). A side-viewing probe
could be created using a combination of a lens and a mirror or
prism or through the use of a convex mirror to replace the
lens-mirror combination. Finally, the entire probe can be made to
rotate radially in order to provide a circumferential scan of the
probed area.
[0191] Yet another data acquisition embodiment could be a fa/LCI
system is based on a modified Mach-Zehnder interferometer as
illustrated in FIG. 15A. The output 10'' from a fiber-coupled
superluminescent diode (SLD) source 12'' (e.g. Superlum, P.sub.o=15
mW, .lamda.o=841.5 nm, .DELTA..lamda.=49.5 nm, coherence length=6.3
.mu.m) is split into sample arm delivery fiber 16'' and a reference
arm delivery fiber 14'' by a 90/10 fiber splitter FS (80') (e.g.
manufactured by AC Photonics). The sample arm delivery fiber 16''
can consist of either of the following for example: (1) a single
mode fiber with polarization control integrated at the tip; or (2)
a polarization maintaining fiber. A sample probe 1113 is assembled
by affixing the delivery fiber 16''(NA.apprxeq.0.12) along the
ferrule 1114 at the distal end of a fiber bundle 1116 such that the
end face of the delivery fiber 16'' is parallel to and flush with
the face of the fiber bundle 1116. Ball lens L1 (1115) (e.g.
f.sub.1=2.2 mm) is positioned one focal length from the face of the
probe 1113 and centered on the fiber bundle 1116, offsetting the
delivery fiber 16'' from the optical axis of lens L1 (1115). This
configuration, which is also depicted in FIG. 15B, produces a
collimated beam 1120 (e.g. P=9 mW) with a diameter (e.g. 2
f.sub.1NA) of 0.5 mm incident on the sample 18'' at an angle of
0.25 rad. for example.
[0192] The scattered light 1122 from the sample is collected by
lens L1 (1115) and, via the Fourier transform property of the lens
L1 (1115, the angular distribution of the scattered field 1122 is
converted into a spatial distribution at the distal face of the
multimode coherent fiber bundle 1116 (e.g. Schott North America,
Inc., length=840 mm, pixel size=8.2 .mu.m, pixel count=13.5K) which
is located at the Fourier image plane of lens L1 (1115). The
relationship between vertical position on the fiber bundle, y', and
scattering angle, 0 is given by y'=f.sub.1.theta.. As an
illustration, the optical path of light scattered 122 at three
selected scattering angles is shown in FIG. 15B. Overall, the
angular distribution is sampled by approximately 130 individual
fibers for example, across a vertical strip of the fiber bundle
16'', as depicted by the highlighted area in FIG. 15C. The 0.2 mm,
for example, thick ferrule (d.sub.1) separating the delivery fiber
16'' and fiber bundle 1116 limits the minimum theoretical
collection angle (.theta..sub.min,th=d.sub.1/f.sub.1) to 0.09 rad
in this example. The maximum theoretical collection angle is
determined by d.sub.1 and d.sub.2, the diameter of the fiber
bundle, by .theta..sub.max,th=(d.sub.1+d.sub.2)/f.sub.1 to be 0.50
rad. Experiments using a standard scattering sample 1122 indicate
the usable angular range to be .theta..sub.min=0.12 rad. to
.theta..sub.max=0.45 rad. d.sub.1., for example, can be minimized
by fabricating a channel in the distal ferrule 1123 and positioning
the delivery fiber 16'' in the channel.
[0193] The fiber bundle 1116 is spatially coherent, resulting in a
reproduction of the collected angular scattering distribution at
the proximal face. Additionally, as all fibers in the bundle 1116
are path length matched to within the coherence length, the optical
path length traveled by scattered light 1122 at each angle is
identical. "The system disclosed in "Fiber-optic-bundle-based
optical coherence tomography," by T. Q. Xie, D. Mukai, S. G. Guo,
M. Brenner, and Z. P. Chen in Optics Letters 30(14), 1803-1805
(2005) (hereinafter "Xie"), incorporated by reference herein in its
entirety, discloses a multimode coherent fiber bundle into a
time-domain optical coherence tomography system and demonstrated
that the modes of light coupled into an individual fiber will
travel different path lengths. In one example, it was
experimentally determined that the higher order modes are offset
from the fundamental mode by 3.75 mm, well beyond the depth
(.about.400 .mu.m) required for gathering clinically relevant data.
Additionally, the power in the higher order modes had a minimal
affect on dynamic range as the sample arm power is significantly
less than the reference aim power. Finally, it should be noted that
while the system disclosed in Xie collected data serially through
individual fibers, the example disclosed herein uses 130 fibers to
simultaneously collect scattered light across a range of angles in
parallel, resulting in rapid data collection.
[0194] The angular distribution exiting a proximal end 1124 of the
fiber bundle 1116 is relayed by the 4 f imaging system of L2 and L3
(f.sub.2=3.0 cm, f.sub.3=20.0 cm) to the input slit 48'' of the
imaging spectrograph 29'' (e.g. Acton Research, InSpectrum 150).
The theoretical magnification of the 4 f imaging system is
(f.sub.3/f.sub.2) 6.67 in this example. Experimentally, the
magnification was measured to be M=7.0 in this example with the
discrepancy most likely due to the position of the proximal face
1124 of the fiber bundle 1116 with relation to lens L2 (126). The
resulting relationship between vertical position on the
spectrograph slit 48'', y, and .theta. is
y=Mf.sub.1(.theta.-.theta..sub.min). The optical path length of the
reference arm is matched to that of the fundamental mode of the
sample arm. Light 1127 exiting the reference fiber 14'' is
collimated by lens L4 (1128) (e.g. f=3.5 cm, spot size=8.4 mm) to
match the phase front curvature of the sample light and to produce
even illumination across the slit 48'' of the imaging spectrograph
29''. A reference field 1130 may be attenuated by a neutral density
filter 1132 and mixed with the angular scattering distribution at
beamsplitter BS (1134). The mixed fields 1136 are dispersed with a
high resolution grating (e.g. 1200 lines/mm) and detected using an
integrated, cooled CCD (not shown) (e.g. 1024.times.252, 24
.mu.m.times.24 .mu.m pixels, 0.1 nm resolution) covering a spectral
range of 99 nm centered at 840 nm, for example.
[0195] The detected signal 1136, a function of wavelength, .lamda.,
and .theta., can be related to the signal and reference fields (Es,
Er) as:
I(.lamda..sub.m,.theta..sub.n)=|E.sub.r(.lamda..sub.m,.theta..sub.n)|.su-
p.2+|E.sub.s(.lamda..sub.m,.theta..sub.n)|.sup.2+2ReE.sub.s(.lamda..sub.m,-
.theta..sub.n)E.sub.r*(.lamda..sub.m,.theta..sub.n)cos .phi.,
(11)
where .phi. is the phase difference between the two fields, (m,n)
denotes a pixel on the CCD, and . . . denotes a temporal average.
I(.lamda..sub.m,.theta..sub.n) is uploaded to a PC using LabVIEW
manufactured by National Instruments software and processed in 320
ms to produce a depth and angle resolved contour plot of scattered
intensity. The processing of the angle-resolved scattered field to
obtain depth and size information described above, and in
particular reference to the data acquisition apparatus of FIGS. 8A
and 8B, can then used to obtain angle-resolved, depth-resolved
information about the sample 18'' using the scattered mixed field
1136 generated by the apparatus in FIGS. 15A-15C.
Dual Window (DW) Techniques
[0196] The DW apparatuses and methods of the embodiments disclosed
herein may be calculated by software executing on a microprocessor
coupled to the spectrographs 112 (FIG. 1A), 29 (FIG. 8A), and 29'
(FIG. 13), as examples. FIG. 42 discussed below at the end of this
disclosure provides a schematic diagram representation of an
exemplary machine in the exemplary form of an exemplary computer
system adapted to execute instructions from an exemplary
computer-readable medium to perform the DW techniques described
herein.
[0197] The DW apparatuses and methods are based on calculating two
or more separate STFTs and then combining the results. In this
example, two STFTs are obtained. The first STFT in this example
uses a broad spectral Gaussian window to obtain high temporal/depth
resolution while the second STFT in this example uses a narrow
spectral window to generate high spectroscopic resolution. The two
resulting TFDs are then multiplied together to obtain a single TFD
with simultaneously high spectral and temporal resolutions.
[0198] Mathematical analysis of this approach shows the DW
technique is equivalent to probing the Wigner TFD with two
orthogonal Gaussian windows, which can be independently tuned in
the spectral and spatial/temporal dimensions, thus avoiding the
tradeoff that hinders the STFT.
[0199] To understand what the DW technique in this example is
revealing, consider the FDOCT signal:
I(k)=I.sub.R(k)+I.sub.S(k)+2E.sub.R(k)E.sub.S*(k)cos(kd), (12)
where I(k) is the total detected intensity, I.sub.R and I.sub.S are
the intensities of the reference and sample fields, respectively,
and d is a constant optical path difference between the sample and
reference arms. The STFT of the cross correlation term,
2E.sub.RE.sub.S*cos(kd). can be expressed as:
S ( k , z ) = .intg. 2 E R ( .kappa. ' ) E S * ( .kappa. ' ) cos (
.kappa. ' d ) - ( .kappa. ' - k ) 2 2 u 2 - .kappa. ' z .kappa. ' .
( 13 ) ##EQU00002##
[0200] Note that u, the width or standard deviation of the Gaussian
window, should be chosen carefully in order to obtain acceptable
spectral or temporal resolution. If, for example, u is chosen to be
the same order of magnitude as the bandwidth of the source, then
the STFT produces a TFD that has good temporal/depth resolution,
but possibly poor spectral resolution. On the other hand, if u is
chosen to be much smaller than the bandwidth of the source, then
the STFT generates a TFD with good spectral resolution, but
possibly poor temporal resolution. The DW technique, however, can
avoid this resolution tradeoff.
[0201] Consider the TFDs resulting from two STFTs, S.sub.1 and
S.sub.2, generated by a narrow spectral window and a wide spectral
window, respectively. Assuming that the reference field in Eq. (12)
is slowly varying over the frequencies of interest, the processed
signal is given by:
DW ( k , z ) = S 1 ( k , z ) S 2 * ( k , z ) = .intg. .intg. 4 E S
* ( k 1 ) E S ( k 2 ) cos ( k 1 d ) cos ( k 2 d ) .times. - ( k 1 -
k ) 2 2 a 2 - ( k 2 - k ) 2 2 b 2 - ( k 1 - k 2 ) z k 1 k 2 , ( 14
) ##EQU00003##
where a and b are independent parameters that set the widths of the
windows, and b>>a. In order to obtain a more insightful form
of the processed signal, consider a coordinate change such
that:
.OMEGA. = k 1 + k 2 2 , q = k 1 - k 2 , k 1 = .OMEGA. + q 2 , and k
2 = .OMEGA. - q 2 , ( 15 ) ##EQU00004##
where the Jacobian of the transform is unity. Thus, the processed
signal DW can be written as:
DW ( k , z ) = .intg. .intg. 4 E S * ( .OMEGA. + q 2 ) E S (
.OMEGA. - q 2 ) cos ( ( .OMEGA. + q 2 ) d ) cos ( ( .OMEGA. - q 2 )
d ) .times. - ( .OMEGA. + q 2 - k ) 2 2 a 2 - qz .OMEGA. q . ( 16 )
##EQU00005##
[0202] The term
E S * ( .OMEGA. + q 2 ) E S ( .OMEGA. - q 2 ) ##EQU00006##
from Eq. (16) can be expressed in terms of a Wigner TPD by
utilizing the ambiguity function [12, 13]:
E S * ( .OMEGA. + q 2 ) E S ( .OMEGA. - q 2 ) = .intg. W S (
.OMEGA. , .zeta. ) - q .zeta. .zeta. , ( 17 ) ##EQU00007##
where W.sub.S(.OMEGA.,.zeta.) is the Wigner TFD of the sample field
in the new coordinate system. After substituting Eq. (17) into Eq.
(16) and simplifying, the processed signal yields:
DW ( k , z ) = .intg. .intg. .intg. 4 W S ( .OMEGA. , .zeta. ) - q
.zeta. .zeta. cos ( 2 .OMEGA. d ) cos ( q d ) .times. - ( ( .OMEGA.
- k ) + q 2 ) 2 ( 1 2 a 2 + 1 2 b 2 ) + q ( .OMEGA. - k ) b 2 - qz
.OMEGA. q . ( 18 ) ##EQU00008##
[0203] By integrating Eq. (18) with respect to q and assuming a is
small compared to b, such that a.sup.2/b.sup.2<<1, the DW
signal simplifies to:
DW ( k , z ) = 4 b .pi. .intg. .intg. W S ( .OMEGA. , .zeta. ) - 2
( .OMEGA. - k ) 2 b 2 - 2 ( d + .zeta. + z ) 2 a 2 cos ( 2 .OMEGA.
d ) .OMEGA. .zeta. . ( 19 ) ##EQU00009##
[0204] Equation (19) shows that the DW technique is equivalent to
probing the Wigner TFD of the sample field with two orthogonal
Gaussian windows, one with a standard deviation of b/2 in the
spectral dimension and another with a standard deviation of 1/(2a)
in the spatial/temporal dimension. Furthermore, a and b
independently tune the spectral and spatial/temporal resolutions,
respectively, thus avoiding the tradeoff that hinders the STFT.
Equation (19) also shows that the processed signal is modulated by
an oscillation that depends on the constant path difference, d,
between the sample and reference arms. This phenomenon is also
observed in the cross terms of the Wigner TED, which have been
identified to contain valuable information about phase differences
[12]. The utility of this oscillatory term is explored below.
[0205] Another interesting result is obtained if a approaches zero
and b is taken to be much larger than the bandwidth of the source,
.DELTA.k. In these limits, the window with standard deviation
a.fwdarw.0 approaches the delta function, while the second window
whose standard deviation b>>.DELTA.k, becomes a constant
across the spectrum. If our signal F(k)=2E.sub.RE.sub.Scos(kd), and
f(z)F(k) is a Fourier transform pair, Eq. (14) yields:
DW ( k , z ) a -> 0 , b >> .DELTA. k = S 1 ( k , z ) a
-> 0 S 2 ( k , z ) b >> .LAMBDA. k = 1 2 .pi. f ( z ) F (
k ) - k z . ( 20 ) ##EQU00010##
[0206] Equation (20) is equivalent to the Kirkwood & Rihaczek
TFD, and if the real part is taken, it is equal to the Margenau
& Hill (MH) TFD [13]. Either of these two distributions can be
simply transformed to produce any of the Cohen's class functions,
such as the Wigner TFD [13].
DW Simulations
[0207] To illustrate the power of the DW technique, two different
simulations are presented. In the first, a signal consisting of two
optical fields separated in time and center frequency is simulated.
The total sample field is given by E.sub.s=E.sub.1+E.sub.2, where
E.sub.1=E.sub.0exp(-z.sup.2)exp(ik.sub.1z),
E.sub.2=E.sub.0exp(-(z-z.sub.0).sup.2)exp(ik.sub.2z), and
k.sub.1>k.sub.2. The Wigner distribution of the total sample
field is given by:
W ( k , z ) = 1 2 .pi. .intg. E S * ( z - .zeta. 2 ) E S ( z +
.zeta. 2 ) k .zeta. .zeta. , ( 21 ) ##EQU00011##
and the MH distribution of the total sample field is given by:
MH ( k , z ) = Re 1 2 .pi. E _ S ( k ) E S ( z ) - kz , ( 22 )
##EQU00012##
where Es(k)E.sub.S(z) is a Fourier transform pair. FIGS. 16A-16D
illustrate the resulting TFDs.
[0208] An example ideal TFD 1200, shown in FIG. 16A, is produced by
treating each pulse as an individual field and superimposing their
respective TFDs onto one map. However, this can be obtained with
prior knowledge of the individual fields. The ideal TFD 1200 in
FIG. 16A contains two pulses 1202, 1204 with Gaussian shapes in
both the temporal and spectral dimensions. The pulses 1202, 1204
are well separated in each dimension. FIGS. 16B-16D show different
exemplary TFDs 1206, 1208, 1210 that can be generated from a single
mixed field. The Wigner distribution 1206, shown in FIG. 16B,
reveals the two Gaussian pulses along with an additional cross term
that appears between them. The cross term contains modulations in
each dimension which, in some cases, reveal important information
about the temporal phase differences [12]. More often, however,
these cross terms are viewed as undesirable artifacts as they yield
non-zero values at times/depths and frequencies that do not exist
in the field. Moreover, as more components are added to the field,
the cross terms may interfere with the local signals.
[0209] The exemplary MH distribution 1208, shown in FIG. 16C,
contains four pulses. In addition to the two pulses comprising the
signal field, the MII TFD 1208 also contains two artifact pulses
known as `reflections in time` [13]. As is the case with the Wigner
distribution, these artifacts yield non-zero intensities at times
and frequencies that should contain no signal.
[0210] The TFD 1210 generated using the exemplary DW technique is
presented in FIG. 16D. The exemplary TFD 1210 is generated by
simply computing the product of two STFTs processed with wide and
narrow spectral windows respectively. In FIG. 16D, the cross terms
that are present in the Wigner and MH distributions 1206, 1208 are
suppressed as a result of the use of two orthogonal windows.
[0211] The second simulation models a SOCT signal from a Michelson
interferometer with an experimental sample containing two distinct
reflecting surfaces. The first sample surface reflects the entire
Gaussian spectrum of the source while the second sample surface
absorbs the high frequency portion (upper half) of the source
spectrum. This simulation is analogous to the absorbing phantom
experiment discussed below. In the scenario of this simulation,
i.e., a SOCT system, neither the Wigner nor the MH distributions
can be constructed because the detected signal is the intensity of
the field and therefore the phase information is lost. Thus, the
TFDs are reconstructed in this example via the STFT and the DW
technique.
[0212] FIG. 17A shows an exemplary ideal TFD 1212 of the simulated
signal while FIGS. 17B and 17C show exemplary TFDs 1214, 1216
generated by the STFT using narrow and wide spectral windows,
respectively. In each case, the effects of the time-frequency
resolution tradeoff are obvious. The TFD generated with the wide
spectral window suffers from degraded temporal resolution while the
TFD generated with the narrow spectral window suffers from degraded
spectral resolution. As Xu et al. showed, the STFT window can be
optimized for specific applications, but regardless of the window
size, a resolution tradeoff must be made [11]. FIG. 17D shows an
exemplary TFD 1218 generated using the DW technique, which computes
the product of the TFDs 1214, 1216 shown in FIGS. 17B and 17C. FIG.
17E shows exemplary time marginals 1220 computed from FIGS.
17B-17D, which demonstrate that the DW technique resolves the two
sample surfaces with a resolution comparable to that of the ideal
case, whereas the narrow spectral window STFT does not. FIG. 17P
shows an exemplary spectral profile 1222 of the rear surface
reflection in FIGS. 17B-17D illustrating that the DW technique
maintains higher spectral fidelity than the wide spectral window
STFT. Note that the DW technique is able to accurately portray the
absorbed wavenumbers, while the wide spectral window STFT reveals
no absorption information. The DW frequency profile also reveals
the same spectral modulation that is seen in the narrow window STFT
and that is characteristic of the Wigner TFD. This modulation
results from cross correlations between field components that
overlap in time and is analyzed further below.
Local Oscillations
[0213] It has been shown previously that temporal coherence
information from Wigner TED cross-terms can be utilized to gain
structural knowledge of samples via the SOCT signal[12]. However,
these cross terms are typically viewed as undesirable artifacts as
they yield non-zero values at times/depths and frequencies that do
not actually exist in the field.
[0214] Equation (19) shows that signals processed by the DW
technique are modulated by a cosine term whose frequency depends on
the constant path difference, d, between the sample and reference
arms. This is the same phenomenon that is observed in the cross
terms of the Wigner TFD, and these oscillations can be used to gain
valuable information about phase differences.
[0215] FIG. 18B shows an exemplary frequency profile 1226 from the
front reflecting surface of the sample in simulation 2 (FIGS.
17A-17F). This frequency spectrum is taken from depth 3 of a TFD
1224 shown in FIG. 18A, which was generated by the DW technique.
The spectral modulation that is present can be further processed to
reveal structural information about the simulated experimental
sample. Fourier transforming the spectrum of the frequency profile
1226 from FIG. 18B generates a correlation plot 1228 shown in FIG.
18C, which exhibits a clear correlation peak corresponding to a
physical distance of 1.5. This distance agrees with the 1.5 unit
spacing of the surfaces in the simulated sample, thus providing
additional information about the structure of the sample.
Experimental Results
Absorption Phantom Experiments
[0216] Exemplary experiments were performed using the white light
parallel frequency domain OCT (pfdOCT) system previously described
by Graf et al. in [15]. To evaluate the ability of the DW
processing method to generate TFDs with simultaneously high
spectral and temporal resolution, an absorption phantom is
constructed consisting of a glass wedge filled with an absorbing
dye 1230, as shown in FIG. 19A. FIG. 19B shows an exemplary pfdOCT
scan 1232 of the absorption phantom with the two inner glass
surfaces clearly visible. Note that the signal from the rear
surface is significantly attenuated at the thicker end of the wedge
due to considerable signal absorption due to the greater volume of
absorbing dye present. Because the experimental system operates in
the visible wavelength band, a visible absorbing dye consisting of
a red food-coloring gel and water solution could be used. FIG. 19C
shows a transmission spectrum 1234 of the absorbing dye, which
shows strong absorption in the high wavenumber range of the
detected spectrum. One would expect signals returning from the
front surface of the phantom to exhibit a relatively flat spectrum,
while signals reflected by the back surface of the phantom would
exhibit spectra with significant attenuation of the higher
wavenumbers, mirroring the absorption spectrum of the dye through
which it passed.
[0217] The raw data corresponding to the position of an exemplary
dashed red line 1236 in FIG. 19B was processed with four different
methods to yield the four TFDs shown in FIGS. 20A-20B. FIG. 20A was
generated using the exemplary STFT processing method with a narrow
spectral window of 0.0405 .mu.m.sup.-1. A resulting exemplary TFD
1238 has excellent spectral resolution, showing a relatively flat
spectrum across all wavelengths at the depth corresponding to the
front surface of the phantom. The sharp spectral cut-off at high
wavenumbers, characteristic of the dye absorption, is evident at
deeper depths. However, the narrow spectral window used to generate
this TFD yields very poor temporal resolution, resulting in an
inability to resolve the two surfaces of the phantom. FIG. 20B was
also processed using the exemplary STFT method, but in this case a
wide spectral window of 0.665 .mu.m.sup.-1 was used. A resulting
TFD 1240 has excellent temporal resolution, clearly resolving the
two surfaces of the phantom. However, the spectral resolution of
the resulting TFD is too poor to resolve the spectral modulation
expected for the rear surface spectrum. FIG. 20C shows the
exemplary TFD generated using the STFT method with a window of
moderate spectral width, 0.048 .mu.m.sup.-1. As expected, the
spectral and temporal resolutions of a resulting TFD 1242 fall
between those of FIGS. 20A and 20B, illustrating the
temporal-spatial resolution tradeoff associated with the STFT
processing method. While the spectral characteristics of the
absorbing dye are apparent in this TFD, the two phantom surfaces
still cannot be resolved.
[0218] An exemplary TFD 1244 in FIG. 20D was generated using the DW
technique. By processing the raw data with both a narrow and a wide
spectral window, the TFD simultaneously achieves high spectral and
temporal resolution. The front phantom surface exhibits a
relatively flat spectrum across all wavelengths while the rear
surface spectrum clearly reveals a spectral cutoff at high
wavenumbers due to the absorbing dye through which the signal field
has passed. Additionally, the front and back surfaces of the
phantom are clearly resolved in depth.
[0219] The utility of the DW processing method is further
demonstrated by examining spectral cross-sections and time
marginals of the generated TFDs. FIG. 21A displays exemplary
spectral profiles 1246 from depths corresponding to the absorption
phantom's rear surface in the TFDs 1242, 1244 of FIGS. 20C and 20D.
For reference, the absorbing dye transmission spectrum is displayed
as well. FIG. 21B shows exemplary spectral cross-sections 1248 from
depths corresponding to the phantom's front surface, along with the
phantom's reflectance spectrum for reference. Exemplary time
marginals 1250 of each TFD 1246, 1248 are displayed in FIG. 21C
along with the corresponding A-scan from FIG. 19B. It is evident
that the TFD generated by the DW technique maintains the ability to
resolve the two peaks of the absorption phantom, while the TED
generated by the STET method does not.
[0220] In addition to limiting the resolution tradeoff associated
with the STET, the exemplary DW technique also achieves an increase
in the spectral fidelity of generated TFDs. The exemplary
normalized spectra from FIGS. 21A and 21B are plotted in FIGS. 22A
and 22B with the high frequency modulation removed by a low-pass
filter. By separating the low frequency content from the high
frequency local oscillations, one can assess the fidelity with
which each processing method recreates the ideal spectrum.
Chi-squared values for each processing method were calculated to
assess goodness-of-fit. Table 1 below summarizes exemplary
chi-squared values. For both exemplary rear surface spectra 1252 in
FIG. 22A and front surface spectra 1254 in FIG. 22B, the
chi-squared values associated with the DW technique are lower than
those of the STFT indicating that the DW processing method
recreates the ideal signal with greater spectral fidelity. In
addition, the goodness of fit for the square of the STFT is
calculated in this example to account for the fact that the DW
technique produces a bi-linear distribution. The exemplary DW
technique is also seen to produce superior spectral fidelity than
the STFT squared.
TABLE-US-00001 TABLE 1 Chi-squared calculations DW STFT Rear
surface spectrum 0.0980 0.1329 Front surface spectrum 0.0248
0.0305
[0221] As with the simulated SOCT signals, the local oscillations
seen in the TFD obtained from probing the absorption phantom (FIGS.
22A and 22B) can also be analyzed to gain structural information
about the experimental sample. FIG. 23B shows exemplary spectral
profile 1256 from the front surface of an absorption phantom 1258
indicated by a dashed red line 1260 in FIG. 23A. Fourier
transforming this spectrum produces an exemplary correlation plot
1262 as shown in FIG. 23C with a clear correlation peak
corresponding to a physical distance of 20.60 .mu.m. This
measurement represents the spacing between the phantom surfaces and
is in excellent agreement with the spacing measured in the OCT
image of the phantom, 20.60 .mu.m.+-.5.97 .mu.M. Here the
measurement uncertainty is larger than the 1.22 .mu.m depth
resolution due to the fact that the glass surface was slightly
abraided to increase the signal, producing a broader range of path
lengths.
Animal Tissue Experiments
[0222] To show the utility of the DW technique for processing SOCT
and fLCI signals from biological samples, the pfdOCT system was
applied in this example to capture spectra from ex vivo hamster
cheek pouch epithelial tissue. The tissue sample was freshly
excised and placed between two coverglasses prior to scanning. Data
was collected without the need for any fixation, staining, or
further preparation of the tissue. The raw data was processed using
the DW technique and resulted in an exemplary TFD 1264 shown in
FIG. 24A.
[0223] The generated TFD can be used to identify spectral
modulation due to scattering within the sample, specifically to
assess nuclear morphology in situ based on scattering signatures.
In epithelial tissues, the majority of nuclear scattering occurs in
the basal layer, approximately 40 .mu.m beneath the tissue surface,
as determined by histopathological analysis. The corresponding
depth of the exemplary TFD 1264 in FIG. 24A was selected and the
spectra from 15 adjacent lines were averaged in order to increase
the signal-to-noise ratio. The averaged spectrum was first fit by a
power-law and an exemplary residual spectrum 1266 is shown in FIG.
24B. The local oscillations present in this signal contain valuable
structural information about the scatterers in the tissue. It has
been previously shown that these local oscillations can be used to
quantitatively determine nuclear morphology by analyzing the
Fourier transform of the spectrum, producing a plot of the
depthwise correlation function [8]. Upon Fourier transforming the
exemplary residual spectrum 1266 from FIG. 24B, a correlation plot
1268 shown in FIG. 24C is obtained, showing a clear correlation
peak corresponding to a mean scatterer diameter of 4.94 .mu.m. This
diameter corresponds nicely with the nuclear diameter expected for
the basal tissue layer of hamster cheek pouch epithelium.
[0224] In summary, the exemplary DW techniques disclosed herein may
be used for processing SOCT signals and can simultaneously maintain
high spectral and temporal resolution. Moreover, the nature of SOCT
signals provides a well-conditioned and optimal problem for the DW
technique, even though it is expected that this approach may break
down for signals with sharply varying frequency content, such as
those due to a chirped pulse. It has been shown that the DW
techniques probe the Wigner TFD of the signal field with two
orthogonal windows that independently determine spectral and
temporal resolution and thus avoid the resolution tradeoff that
hinders traditional SOCT and fLCI processing methods. In addition,
it has been shown that local oscillations contained in the TFDs
generated by the DW technique contain valuable information about
the structure of experimental samples. By comparing the performance
of the DW and STFT processing methods in analyzing SOCT signals
from an absorption phantom, it has been shown that the DW technique
recovers TFDs with superior fidelity while simultaneously
maintaining high spectral and temporal resolution. It has also been
shown the utility of the DW technique for processing SOCT and fLCI
signals from biological samples to gain morphological information
about scatterers.
[0225] Since its introduction, SOCT has held promise for gaining
spatial and functional knowledge of a biological sample by mapping
spectral information onto depth resolved images. Unfortunately,
traditional SOCT processing methods such as the STFT and CWT have
been limited by an inherent tradeoff between spectroscopic and
depth resolution. This time-frequency tradeoff greatly reduces the
utility of the analysis by degrading either the depth or spectral
resolution to the point that important features cannot be
accurately reconstructed. It is expected that by avoiding this
tradeoff, the DW processing method will enable new directions in
SOCT and depth resolved spectroscopy.
[0226] The exemplary DW techniques disclosed herein have been used
to process measurements of morphological features in a thick turbid
sample using light scattering spectroscopy (LSS) and Fourier-domain
low coherence interferometry (fLCI). A parallel frequency domain
optical coherence system with a white light source is used to image
a two-layer phantom containing polystyrene beads of diameters 4.00
.mu.m and 6.98 .mu.m on the top and bottom layers, respectively.
The DW technique decomposes each OCT A-scan into a time-frequency
distribution with simultaneously high spectral and spatial
resolution. The spectral information from localized regions in the
sample is used to determine scatterer structure. The results show
that the two bead populations can be accurately and precisely
differentiated using LSS and fLCI.
[0227] Light scattering spectroscopy (LSS) [17] has served as one
exemplary foundation for a number of technologies including
Fourier-domain low-coherence interferometry (fLCI) [18], which has
been developed to measure the enlargement of epithelial cell nuclei
associated with precancerous development [19]. In fLCI, depth
resolution is obtained by coherence gating with spectral
information acquired using a short time Fourier transform (STFT).
This process is similar to what is done in spectroscopic optical
coherence tomography (SOCT) [20]. However, in fLCI, after
processing with a STET, the spectrum from a given depth is
quantitatively analyzed to determine the size of scattering objects
[18].
[0228] SOCT, an extension of optical coherence tomography, provides
the same cross-sectional tomographic imaging capabilities of OCT
[21] with the added benefit of spectroscopic based contrast [20].
As described above, SOCT uses STFTs or wavelet transforms to obtain
spectroscopic information, which provides additional information
about a sample. Unfortunately, the windowing process of STFTs
introduces an inherent trade off between spatial and spectral
resolution, which limits further quantitative processing of the
depth resolved spectra. The dual window (DW) method for processing
SOCT signals achieves both high spectral and spatial resolution,
allowing for a more thorough quantitative treatment of the depth
resolved spectral information [22].
[0229] Morphological measurements of different populations of
scatterers in a turbid medium may be processed with the DW
technique, and analyzed with LSS and fLCI techniques. The DW
technique decomposes each depth resolved A-scan from the OCT signal
into a time-frequency distribution (TFD), which inherently aligns
the quantitative spectral analysis with the OCT image to determine
the local scatterer structure. The approach is demonstrated through
imaging and analysis of a two-layer phantom, with each layer
containing a suspension of different size polystyrene beads.
[0230] A white light parallel frequency domain OCT system, as
described by Graf et al [23], can be used. In short, a Michelson
interferometer geometry can be modified with four additional
lenses. to form a 4 F imaging system, thereby limiting the number
of spatial modes illuminating the sample and reference arm. In this
example, the light returned by the two arms are combined and imaged
onto the entrance slit of an imaging spectrograph. The interference
signal is obtained in parallel across one dimension comprising 150
spatial lines and spanning 3.75 mm. The spectrograph can disperse
each channel into its wavelength components, where a 150 nm
bandwidth centered at .lamda..sub.0=550 nm is analyzed, yielding an
axial resolution of 1.22 .mu.m. The spectrograph may be configured
to disperse each channel into color channels, such as for red,
green, and blue wavelength components that can be used to display
information using RGB values on an RGB display
[0231] To process the OCT image, six steps can be taken as an
example. 1. The sample and reference arm intensities are acquired
separately and subtracted from the signal. 2. The resulting
interferometric signal is divided by intensity of the reference
field to normalize for the source spectrum and detector
efficiencies as a function of .lamda.. This step is of particular
importance for quantitative comparison of depth resolved spectra,
since the remaining spectral dependence is assumed to arise solely
from absorption of forward scattered light and scattering cross
sections of backscattered light. 3. The data are re-sampled into a
linear wave-number vector, k=2.pi./.lamda.. 4. Chromatic dispersion
is digitally corrected. 5. A fast Fourier transform is executed to
obtain an A-scan, and 6. The process can be repeated for each of
the 150 spatial lines to obtain the OCT image.
[0232] Similar to the generation of the OCT image, the exemplary DW
technique can use the interferometric information and provide
exemplary steps 1-4, as described above. As a last step, a product
of two STFTs is taken: one STFT with a narrow window for high
spectral resolution and another with a wide window for high spatial
resolution. Eq. 23 describes the distribution obtained with the
exemplary DW technique from a single spatial line,
DW ( k , z ) = .intg. 2 E S cos ( .kappa. 1 .DELTA. O P L ) - (
.kappa. 1 - k ) 2 2 a 2 - .kappa. 1 z .kappa. 1 .times. .intg. ( 2
E s cos ( .kappa. 2 .DELTA.O P L ) - ( .kappa. 2 - k ) 2 2 b 2 -
.kappa. 2 z ) * .kappa. 2 , ( 23 ) ##EQU00013##
where z is the axial distance, and a and b are the standard
deviations of the windows. Robles et al. have shown that the DW, a
product of two linear operations, can be described by Cohen's class
bilinear functions [22]. With b>>a, the DW samples the Wigner
TED with two orthogonal windows that are independently set by the
parameters a and b, resulting in suppression of many common
artifacts.
[0233] The exemplary DW contains two components that relay
information, which are analyzed independently in this example. The
first component, contained in the low frequencies of the DW(k,
z.sub.0), corresponds to the spectral dependence of the optical
signal at z.sub.0 and arises from absorption and scattering in the
sample. This component is analyzed with LSS. The second component
is the morphological features about z.sub.0, arising from the
temporal coherence of the scattered light and contained in the
local oscillations (high frequencies) of the signal [22]. This is
analyzed with fLCI.
[0234] This study seeks to analyze scattering structures in a thick
turbid sample using LSS and fLCI methods. Thus, a two-layer phantom
containing polystyrene beads (n.sub.b=1.59) of different sizes
(d=6.98 .mu.m and 4.00 .mu.m in top and bottom layers respectively)
suspended in a mixture of Agar (2% by weight) and water, with
n.sub.a=1.35, is used. The scatterer concentration is chosen to
yield a mean free scattering path length of l.sub.s=1 mm to ensure
sufficient SNR at deeper depths. FIG. 25A shows an OCT image 1270
of the phantom acquired by a single 0.3-sec exposure, with no
scanning needed.
[0235] The exemplary DW technique can be used to calculate a TFD
for each lateral line, yielding a spectrum for each point in the
OCT image with high spectral and spatial resolution (DW parameters
set to a=0.0454 .mu.m.sup.-1 and h=0.6670 .mu.m.sup.-1). FIG. 25B
shows a processed TFD 1272 of a representative line 1276 (dashed
red line in FIG. 25A), with a corresponding A-scan 1280 (FIG. 26B).
Two representative points are selected and the spectrum from each
is analyzed as an example. FIGS. 27A and 27B give spectral profiles
1282, 1284 (solid blue lines) from points 1 and 2,
respectively.
[0236] The low frequencies of the depth resolved spectra contain
information about absorption and scattering cross sections in this
example. Since no chromophores are present, the spectral dependence
gives the scattering cross section of the beads; thus, the Van de
Hulst approximation [24] can be used to determine the bead size. To
achieve this, the DW spectral profile is low-pass filtered with a
hard cut off frequency of 3.5 .mu.m (three cycles); then, a
least-squares fit is used to obtain the scatterer diameter. In
FIGS. 27A and 27B, dotted green lines 1286, 1288 show the low pass
filtered data used for fitting, which yield d.sub.1=3.97 .mu.m and
d.sub.2=6.91 .mu.m for points 1 and 2, respectively, in good
agreement with the true bead sizes. The dashed red line gives the
theoretical scattering cross section corresponding to the best
fits: note that these are in excellent agreement with the processed
signals.
[0237] The high frequency components of DW(k,z.sub.0) in this
example give the fLCI measurement. First, the spectral dependence
is removed by subtracting the line of best fit from the analysis
above. Then, the residuals are Fourier transformed to yield a
correlation function where the maxima give the distance between
dominant scattering features in the analyzed region. For the bead
phantom, the local oscillations predominately result from
scattering by the front and back surfaces. Further, simulated OCT
images by Yi et al., show that a single microsphere gives rise to
multiple peaks [25] which are also taken into account. FIGS. 27C
and 27D plot a correlation function 1290, 1292 for points 1 and 2
respectively, giving correlation peaks at
d.sub.c=.DELTA.OPL/(2n.sub.b)=4.25 .mu.m and 6.87 .mu.m, in good
agreement with both the LSS measurements and true bead sizes.
[0238] The procedure in this example was repeated for all points in
the OCT image, where an automated algorithm selected peaks that
were above a threshold (int.>100) and 10% higher than other
maxima in the correlation function. Further, only points where the
LSS and fLCI measurements were in agreement within the system's
resolution (.+-.1.22 .mu.m) were considered. FIG. 25B shows an
overlay 1272 of the fLCI measurements with the OCT image. In the
top layer, the average scatterer size was 3.82.+-.0.67 .mu.m and
3.68.+-.0.41 .mu.m for the fLCI and LSS measurements, respectively,
with 82% agreement (112 points). In the bottom layer, the average
sctterer size was 6.55.+-.0.47 .mu.m and 6.75.+-.0.42 .mu.m for
fLCI and LSS, respectively, with a lower 35% agreement (113 points)
due to the lower SNR at the deeper sample depth. These results show
that by utilizing two independent methods to analyze scattering
structure (fLCI and LSS), our technique yields accurate and precise
measurements throughout the whole OCT image. Sources of error for
the fLCI measurement can arise due to partial volume effects where
multiple beads lie within a single pixel region (25
.mu.m.times.1.15 .mu.m) giving multiple maxima in the correlation
function.
[0239] In summary, accurate measurements of morphological features
with wavelength precision using LSS and fLCI by processing with the
exemplary DW technique have been achieved. Recently, Yi et al.
presented results that use a similar optical system and STFT
processing to discriminate fluorescent and non-fluorescent
microspheres in a weakly scattering medium [25]. The Yi et al.
analysis was restricted to a thin (<100 .mu.m) layer and did not
assess structure, as they intentionally discarded the high
frequency spectral modulations due to the scatterer's structure
(i.e. diameter). In comparison, the results presented here confirm
the potential to measure enlargement of epithelial cell nuclei,
which are non-absorbing, to detect precancerous development within
intact tissues.
[0240] The novel dual window approach disclosed herein has also
been used for spectroscopic OCT measurements and applied to probe
nuclear morphology in tissue samples drawn from the hamster cheek
pouch carcinogenesis model. The dual window approach enables high
spectral and depth resolution simultaneously, allowing detection of
spectral oscillations which are isolated to determine the structure
of cell nuclei in the basal layer of the epithelium. The
measurements were executed with our parallel frequency domain OCT
system which uses light from a thermal source, providing high
bandwidth and access to the visible portion of the spectrum. The
structural measurements show a highly statistically significant
difference between untreated (normal) and treated
(hyperplastic/dysplastic) tissues, indicating the potential utility
of this approach as a diagnostic method.
[0241] Cancers typically develop slowly over time, beginning with
just a few abnormal cells that grow and proliferate. The majority
of malignancies develop through precancerous states characterized
by varying levels of architectural and cytologic abnormality. [27]
Detecting these structural changes in tissues at the earliest
possible stages could provide an increased opportunity for
therapeutic intervention and thus, greatly reduce rates of
mortality and morbidity. However, detecting precancerous
development is a great challenge for available screening
techniques.
[0242] The current "gold standard" for detecting cancer of
epithelial tissues is the histopathologic analysis of biopsy
samples. Biopsy samples are excised from the tissue under
examination and then fixed, sectioned, stained, and ultimately
examined by a pathologist for morphological abnormalities. Although
this procedure is the standard practice for cancer diagnosis, there
are several drawbacks to this approach, including the subjectivity
of diagnoses, the inherent invasiveness of biopsies, the time delay
between biopsy and diagnosis, and the poor coverage of at-risk
tissue.
[0243] It is clear that improved screening and diagnostic
technologies are needed to overcome these limitations. In recent
years, large amounts of research have focused on developing optical
methods for early cancer detection [28-30] because such methods
hold great promise to overcome the limitations of the traditional
biopsy listed above. One specific technique, elastic light
scattering spectroscopy, is an optical technique that analyzes
scattered light to obtain information about the structures with
which the light interacts. For decades, elastic light scattering
has been utilized in a variety of applications where direct
measurement of physical properties is impractical or impossible.
Most recently, advances in biophotonics have enabled application of
elastic light scattering to biology and medicine. Using powerful,
broadband light sources, elastic scattering spectroscopy (ESS) has
been used by several groups to investigate the cellular morphology
of in vivo and ex vivo tissue samples [31-34]. Because enlargement
of the nuclear diameter is a key indicator of precancerous growth
[27], the morphology of the cell nucleus has become a strategic
target for light scattering studies.
[0244] These advancements have paved the way for an elastic light
scattering technique known as Fourier domain low coherence
interferometry (fLCI) [35, 36]. The fLCI approach uses
interferometry to obtain depth-resolved spectroscopic information
which can then be analyzed to recover structural information, such
as nuclear morphology, from specific layers in a sample. For early
cancer detection, fLCI may be applied to detect enlargement of
nuclear diameter which can serve as a biomarker of precancerous
transformation. This biomarker, either alone or in conjunction with
other information derived from the light scattering signal, can
provide the quantitative information necessary to distinguish
between normal and dysplastic epithelial tissue with high
sensitivity and specificity.
[0245] The results of the first study assessing the ability of the
fLCI technique to distinguish between normal and dysplastic ex vivo
epithelial tissues is hereby presented. In the study, quantitative
nuclear morphology measurements are used as a biomarker to
distinguish between normal and dysplastic hamster cheek pouch
epithelium.
Materials and Methods
Animal Model
[0246] The animal study was completed using the hamster cheek pouch
carcinogenesis model. For the animal study, all experimental
protocols were approved by the Institutional Animal Care and Use
Committees of Duke University and North Carolina Central University
and in accordance with the National Institutes of Health (NIH).
Male Syrian golden hamsters, six weeks of age, were obtained from
Harlan Laboratories (Indianapolis, Ind.) and housed at North
Carolina Central University. The animals were housed four per cage
in a room with controlled temperature and humidity and in a twelve
hour light/dark cycle. Regular cage changes ensured maintenance of
hygienic conditions. All animals were given the AIN-93M diet
(Research Diets, New Brunswick, N.J.). The diet consisted of 14%
casein, 0.18% 1-cystine, 49.5% corn starch, 12.5% maltodextrim 10,
10% sucrose, 5% cellulose, 4% soybean oil, 0.0008%
t-Butylhydroquinone, 3.5% mineral mix, 1% vitamin mix, and 0.25%
choline bitartrate. Tap water was available ad libitum. After an
acclimatization period of one week, the left cheek pouch of each
animal was topically treated with 100 .mu.l of 0.5%
7,12-dimethylbenz[.alpha.] anthracene (DMBA) (Sigma Chemical
Company, St. Louis, Mo.) in mineral oil with a paintbrush three
times per week for six weeks. The right cheek pouch was left
untreated and served as the control group.
Experimental Protocol
[0247] At 24 weeks after the initial treatment of DMBA, the
hamsters were shipped to Duke University for optical spectroscopic
analysis. The hamsters were euthanized by CO.sub.2 asphyxiation
before being subjected to gross necropsy. The entire left and right
cheek pouches were excised and cut into two pieces. The samples
were laid flat between two coverglasses, moistened with PBS, and
immediately scanned by the parallel frequency domain optical
coherence tomography (pfdOCT) system. Following the optical
measurements, scanned areas were marked with India ink and the
tissue samples were fixed in 10% PBS buffered formalin. The fixed
samples were later embedded in paraffin, sectioned, and stained
with hematoxylin and eosin (H & E) for histopathological
analysis.
[0248] The complete animal trial analyzed tissue samples from 21
hamsters. Although one treated and one untreated sample was
extracted from each animal and scanned by the fLCI system, only 16
of 21 untreated samples were used in the study. The signal-to-noise
ratio of the scans from the remaining five untreated samples was
insufficient to provide useful data. Therefore, these scans were
not included in the spectroscopic analysis.
Parallel Frequency Domain Optical Coherence Tomography
[0249] Ex vivo tissue samples were examined using the pfdOCT system
first described by Graf et al. [37] A pfdOCT system 2800, shown in
FIG. 28, is based on a modified Michelson interferometer geometry
and utilizes a 4 f interferometer first demonstrated by Wax, et al.
[38] The system utilizes a light source 2802, which in one
embodiment may be a Xenon arc-lamp source (150 W, Newport Oriel,
Stratford, Conn.) for illumination. The 4 f interferometer uses two
4 f imaging systems to spatially resolve light from the light
source 2802 to the detector. The system 2800 may also include a
beamsplitter 2804; lenses 2806, 2808, 2810, 2812, and 2814; and a
reference mirror 2816. The system 2800 of FIG. 28 may be used to
examine a sample 2817. The detection plane of the imaging system
coincides with an entrance slit 2822 of an imaging spectrometer
2820, which in one embodiment may be a spectrometer such as model
Shamrock 303i, Andor Technology, South Windsor, Conn., which
spatially resolves 255 detection channels, each 25 .mu.m in width.
The entrance slit 2822 allows only a small slice of incoming light
to enter the imaging spectrometer 2820. The imaging spectrometer
2820 includes optics, along with the combination of the 600
lines/mm grating and the 1024 pixel CCD array, and limits the
detected spectrum to the 500-625 nm range. Data from the imaging
spectrometer 2820 may be downloaded in real time to a laptop PC via
a USB 2.0 interface, and spectrometer control and data acquisition
may be achieved using custom LabVIEW (National Instruments, Austin,
Tex.) software.
[0250] The fLCI method seeks to recover structural information
about scatterers by examining the wavelength dependence of the
intensity of elastically scattered light. The technique determines
scatterer sizes by analyzing the Fourier transform of the spectra
originating from specific subsurface layers of a sample. Depth
resolution is obtained by employing the coherence gating methods
commonly used in frequency domain OCT. By exploiting the low
temporal coherence length of a broadband light source in an
interferometry scheme, fLCI can selectively analyze spectral
information from the most diagnostically relevant layers in probed
samples.
[0251] In order to perform depth resolved spectroscopy, fLCI data
must be processed to simultaneously obtain depth resolution and
spectral resolution, from data acquired in a single domain. To
implement this processing, fLCI and spectroscopic OCT have
typically employed a short-time Fourier transform (STFT) in which a
Gaussian window is applied to the interference signal before taking
a Fourier transform, yielding a depth scan centered about a
particular center wavenumber. By shifting the center of the
Gaussian window and repeating the process, a data set with both
depth and spectral resolution can be generated. It should be noted,
however, that with this approach any attempt to increase spectral
resolution results in degradation of depth resolution and vice
versa. Most recently, Robles et al. introduced the Dual Window (DW)
method for processing spectroscopic OCT (SOCT) signals, which can
be incorporated to the fLCI analysis [39]. The DW technique is
based on performing two separate STFTs and combining the results to
achieve simultaneously high depth and spectral resolution.
[0252] From the depth resolved spectroscopic information, fLCI
seeks to determine structural information by analyzing oscillations
in the spectrum of light returned from a specific depth of
interest. More specifically, fLCI seeks to distinguish between
normal and dysplastic epithelial tissue by detecting the nuclear
enlargement that occurs at the earliest stages of precancerous
development. FIG. 29A shows an illustration 1300 representing two
nuclei 1302, 1304 as well as the scattering events that take place
at both a front and back surface 1306, 1308 (for nucleus 1302) and
1310, 1312 (for nucleus 1304) of each nucleus 1302, 1304 where an
index of refraction change is present. Depending on the coherence
of the field induced by the sample [40], the reflections from the
front and back surfaces 1306, 1308, 1310, 1312 of the nuclei 1302,
1304 will interfere with one another, producing constructive or
destructive interference 1314, as shown in FIG. 29B. The frequency
of this oscillation is directly dependent on the diameter and
refractive index of the scatterer with larger particles resulting
in a higher frequency of oscillation and smaller particles
resulting in a lower frequency of oscillation. The fLCI method
seeks to detect and analyze these spectral oscillations to measure
nuclear diameter.
Data Processing
[0253] The raw data acquired by the pfdOCT system consisted of 120
spectra, each of which originates from adjacent 25 .mu.m diameter
spatial points on the experimental sample. The raw interference
data 1314, along with the plots of three such spectra 1316, 1318,
1320, are shown in FIG. 30A. The diameter of the signal beam was
shaped to illuminate only 120 of the 255 spectral channels of the
imaging spectrometer to preserve the signal to noise ratio of the
measurements.
[0254] To analyze spectra from specific tissue layers in this
example, the spectrum detected by each channel of the imaging
spectrometer was processed using the DW technique [39]. Briefly,
the DW technique uses the product of two STFTs to reconstruct the
time-frequency distribution (TFD) of the interferometric signal:
one STFT with a narrow window for high spectral resolution and
another with a wide window for high spatial resolution. Equation 24
gives a mathematical description of the distribution obtained with
the DW technique from a single spatial line,
DW ( k , z ) = .intg. 2 E S cos ( .kappa. 1 .DELTA. O P L ) - (
.kappa. 1 - k ) 2 2 a 2 - .kappa. 1 z .kappa. 1 .times. .intg. ( 2
E s cos ( .kappa. 2 .DELTA.O P L ) - ( .kappa. 2 - k ) 2 2 b 2 -
.kappa. 2 z ) * .kappa. 2 , ( 24 ) ##EQU00014##
with a and b given as the standard deviations of the windows. In
this particular arrangement, the spectral resolution is limited by
the actual resolution of the spectrometer used while the depth
resolution is limited by the coherence length of the detected
light.
[0255] Robles et al. have shown that the distribution obtained from
the DW technique can be related to Cohen's class of bilinear
functions [39], even though it is constructed using two linear
operations. In one limit, where a.sup.2/b.sup.2<<1, the DW
distribution gives a measurement of the Wigner TFD with spectral
and depth resolution set independently by the width of the two
orthogonal windows, a and b. Significantly, the use of the two
orthogonal windows eliminates many common artifacts in other TFD's,
such as the cross term artifacts from the Wigner TFD and the
reflections in time artifacts from the Margenau & Hill TFD.
Further, the DW contains local oscillations in the spectral
dimension, which reveal morphological information about the sample;
specifically, the distance between scattering surfaces in the
vicinity to the point of analysis.
[0256] The exemplary DW technique was implemented using a custom
Matlab program to process the data with both a narrow spectral
window of 0.0405 .mu.m.sup.-1 FWHM and a wide spectral window of
0.665 .mu.m.sup.-1 FWHM. The depth resolved spectra generated by
each window were multiplied together to produce a plot with
simultaneously high spectral and depth resolution. Resulting 120
depth resolved spectroscopic plots 1322, 1324, 1326 were summed
together to improve the signal-to-noise ratio, producing a single
depth resolved spectroscopic plot 1328 for each tissue sample as
shown in FIG. 30B.
[0257] In neoplastic transformation, nuclear morphology changes are
first observed in the basal layer of the epithelial tissue. In
hamster buccal pouch tissue, the basal layer lies approximately 30
to 50 .mu.m beneath the surface for normal tissue, and
approximately 50 to 150 .mu.m beneath the surface for dysplastic
tissue. Because examination of the basal layer offers the earliest
opportunity for detecting developing dysplasia, it is the target
tissue layer for the fLCI technique and for this study.
[0258] In order to target the basal layer of the epithelium, the
raw experimental data were first processed to yield a parallel
FDOCT image by a line-by-line Fourier transform. These `B-mode`
images were summed across the transverse axis to generate single
depth plots (A-scan) like those presented in FIGS. 31A and 31B.
Several important histological features can be identified in the
depth scans and co-registered with the corresponding histopathology
images. FIGS. 31A and 31B indicate the location of a keratinized
layer 1340, 1342 (green arrow), a basal layer 1344, 1346 of the
epithelium (red arrow), and underlying lamina propria 1348, 1350
(blue arrow) in the micrographs of fixed and stained histological
sections from untreated and treated tissue samples. Scattering
peaks corresponding to the same tissue layers were identified in
each depth scan. To correlate the distances in the histology images
with distances in the depth scans, the index of refraction of the
tissue was taken into account. An average refractive index for the
tissue of n=1.38 was used to convert depth scan distances to
optical path lengths [41, 42]. Variation of the refractive index
within the tissue is a potential limitation of the current method
and is discussed further below.
[0259] For each sample, a 15 .mu.m depth segment corresponding to
the location of the basal layer was selected from the depth scan
and used to guide analysis of a depth resolved spectroscopic plot
1352, as shown in FIG. 32A. The spectra from the depth identified
with the basal layer in each A-scan were averaged to generate a
single spectrum for light scattered by the basal layer. As shown in
FIG. 32B, a power law curve 1354 of the form y=bx.sup.a was
initially fit to each spectrum, modeling the spectral dependence
resulting from the fractal structure of cellular organelles
[43-45], including heterogeneity of the sub-structure of the
nucleus. The residual of each spectrum was calculated by
subtracting the power law curve from the experimental spectrum to
produce a normalized spectrum 1356 which isolates the oscillatory
features as shown in FIG. 32C.
[0260] The normalized spectra showed clear oscillations resulting
from interference produced by scattering from the front and back
surfaces of basal cell nuclei. Each normalized spectrum was Fourier
transformed to generate a correlation plot 1358 similar to that
shown in FIG. 32D, which shows a clear peak corresponding to the
dominant frequency in the normalized spectrum. Peak detection was
carried out by an automated, custom Matlab program (Mathworks,
Natick, Mass.). The script first high-pass filtered the spectrum
with a cutoff of 4 cycles in order to remove any low frequency
content not removed by the power law fit. The location of the peak
in the correlation plot was then automatically detected by the
Matlab script and related to scatterer diameter with the simple
equation d=correlation distance/(2n), where n is the refractive
index and d is the diameter of the cell nuclei. An nuclear index of
refraction of n=1.395 was assumed (9).
Results
[0261] The results of the complete animal trial are summarized in
Table 2 and presented graphically in chart 1360 in FIG. 33.
TABLE-US-00002 TABLE 2 Summary of nuclear diameter measurements
from the complete animal trial. Untreated Treated N 16 21 Mean
(.mu.m) 4.28 9.50 Std. Dev 0.69 2.08 p-value <0.0001**
[0262] The sixteen (16) untreated tissue samples had a mean basal
layer nuclear diameter of 4.28 .mu.m with a standard deviation of
0.69 .mu.m. The 21 treated tissue samples had a mean basal layer
nuclear diameter of 9.50 .mu.m with a standard deviation of 2.08
.mu.m. A statistical t-test revealed a p-value of less than 0.0001,
indicating a highly statistically significant difference between
the basal layer nuclear diameters of the two populations.
Histological analysis revealed that untreated samples appeared as
unaltered epithelium while the treated samples all showed a
diseased tissue state ranging from inflammation and hyperplasia to
dysplasia.
[0263] FIG. 33 plots each treated (blue square) tissue sample 1362
and untreated (red x) tissue sample 1364 as a function of its
measured basal layer nuclear diameter. The presented decision line
results in excellent separation between the normal and diseased
samples. Using the indicated decision line, the study results
correctly categorize 21 of 21 treated samples, providing 100%
sensitivity and correctly categorize 16 of 16 untreated samples
providing 100% specificity.
Discussion
[0264] The experimental results of the complete animal trial show
that fLCI has great potential as a technique for distinguishing
between normal and dysplastic epithelial tissues. Experimental
measurements showed an excellent ability to precisely and
accurately distinguish between treated and untreated animal tissue
using in situ measurements of nuclear diameter as a biomarker. The
measured diameters correspond nicely with the nuclear diameter
expected for the basal tissue layer of hamster check pouch
epithelium [46] when measurements are adjusted to account for
fLCI's measurement of the minor axis of cell nuclei. [47] It should
be noted that the development of dysplasia results in thickening in
the basal tissue layer and a breakdown of cellular organization. As
a result, fLCI measurements likely probe the major axis of some
nuclei in diseased tissue, further contributing to the detected
nuclear enlargement when compared with normal tissue.
[0265] The use of the DW technique to extract depth resolved
spectra from animal tissue data is an important advance. The DW
processing method permitted the measurement of spectral
oscillations induced by nuclear scattering that could not be
detected in data processed with the STFT. fLCI data processed with
the STFT suffers from an inherent tradeoff between spectral
resolution and depth resolution. As a result of this tradeoff,
achieving an acceptable spectral resolution necessarily requires
the degradation of depth resolution to the point that spectral
oscillations induced by nuclear scattering are washed out. This
washout is likely due to phase and frequency differences in the
spectra originating from the different tissue layers, which were
combined as a result of the poor depth resolution. In contrast, the
DW technique produced depth resolved spectroscopic plots with
simultaneously high depth and spectral resolutions. The DW
technique generated satisfactory spectral resolution while
maintaining high depth resolution, therefore permitting the
spectral analysis of thin tissue segments. By avoiding the unwanted
combination of signals from many tissue layers, the oscillatory
components of spectra originating from the basal tissue layer were
preserved and available for analysis.
[0266] Though the results of the animal study are extremely
promising, the current methods are not without limitation. The
dependence on refractive index in selecting tissue layers of
interest is a challenge that must be further examined in the
future. The current fLCI data processing algorithm does not account
for potential variations of refractive index within a tissue. The
current method also does not adjust for potential index changes
induced by the onset of dysplasia which also may be a confounding
factor. In order to accurately measure optical path lengths within
a tissue sample, a dynamic model of refractive index must be
developed. Similarly, a robust method to account for the varying
thickness and location of the basal layer during neoplastic
transformation should be implemented.
[0267] Additionally, a more complex model of scatterers within the
tissue should be developed for future studies. Other light
scattering research [47-49] indicates that, in addition to spectral
modulations, spectral shape can yield insight into tissue
micro-architecture and health. Developing a light scattering model
that can capture this information will be a priority as the fLCI
technology is further developed.
Although the detection of peaks in the correlation plots for this
study was automated to eliminate bias, subsequent analysis of the
correlation data revealed that some plots contained multiple
prominent peaks. Understanding how correlations between neighboring
cellular structures and correlations between tissue layers
contribute to generated correlation plots will facilitate the
development of an advanced scattering model.
[0268] It is believed that the correlation peak represents nuclear
diameter, as opposed to the separation between nuclei, for three
primary reasons. First, the front and back surfaces of each nucleus
are relatively well aligned for interference in the axial
direction, whereas the alignment between different nuclei is not as
well ordered and therefore less likely to produce oscillations in
the spatially averaged spectrum. Second, because the distances
between nuclei would have a much larger variation than the
diameters of individual nuclei, it is expected that the separation
between nuclei to yield a much broader distribution of distances
rather than the narrow correlation peaks seen in the correlation
plots. Finally, this study finds that the correlation peak shifts
to longer distances for treated (diseased) samples while remaining
at smaller distances for normal samples. This finding is consistent
with the measurement of nuclear enlargement seen in hyperplastic
and dysplastic tissues. On the other hand, if the correlation plot
was measuring nucleus-to-nucleus correlation, it is expected to see
the peak shift to smaller distances in diseased tissue due to the
increase in nucleus-to-cytoplasmic ratio observed in dysplastic
tissue.
[0269] The results of this study demonstrate fLCI's ability to
distinguish between normal and diseased (DMBA-treated) epithelial
tissue with high sensitivity and high specificity. The in situ
nuclear morphology measurements are acquired without the need for
exogenous staining agents or fixatives. The ability of the fLCI
technique to make quantitative nuclear morphology measurements
demonstrates its potential as an effective technology for
non-invasively detecting dysplasia using an optical measurement.
The results of these experiments lay the groundwork for further
development of fLCI into a technique for clinical diagnostic
applications such as the detection of early cancer development.
[0270] The techniques described herein can also be used to detect
early cancerous cells development. For example, experiments were
performed using the techniques described herein to detect early
colorectal cancer development in an azoxymethane rat carcinogenesis
model with fLCI.
[0271] Colorectal cancer (CRC) is the third most common cancer and
the third leading cause of cancer death in men and women in the
United States [50]. As is commonly known, the most successful
practice for preventing cancer mortality is to regularly screen
people at risk. This is particularly important for CRC since the
disease is largely asymptomatic until it has reached an advanced
stage; fortunately, if diagnosed early, the survival rate
dramatically improves. Today, the gold standard for screening CRC
is conventional colonoscopy, which relies on visual inspection
through an endoscope to detect polyps and adenomas. Once
identified, the decision to remove these mucosal growths is based
on size, where it is recommended that lesions >5 mm in diameter
be removed [51]. This approach, however, suffers from serious
weaknesses: 1. There is no reliable metric for determining whether
lesions are adenomatous or metaplastic; hence, the decision to
remove these lesions is left to the discretion of the physician.
Note that approximately 90% of all cases of CRC originate through
benign adenomas [51]. 2. Despite the fact that small lesions (<5
mm) are not typically removed, some studies have presented evidence
that these are very likely to contain neoplasias, particularly for
lesions proximal to the left colon [52]. 3. Flat adenomas, which
are ten times more likely to contain malignancy compared to
similarly sized polyps, appear similar to the surrounding tissue,
and are consequently very difficult to detect with colonoscopy
[53]. 4. Because all detected polyps are considered adenomatous
[51], many unnecessary biopsies and polypectomies are performed,
which increase the probability of complications [54]. Lastly, while
other screening tests are available, including fecal occult blood
tests, sigmoidoscopy, and virtual colonoscopy, these are more
limited and less effective; further, in the event that an
abnormality is detected with these alternative screening tests,
patients must then undergo a colonoscopy [55].
[0272] The weaknesses of colonoscopy, as described above, highlight
the need for technologies that assess tissue health quantitatively
and in a minimally invasive manner. To this end, biomedical optics
has emerged as a promising field, in which various techniques have
been developed to probe different biomarkers accessible via optical
absorption and/or scattering measurements. For example,
4-dimensional elastically scattered light fingerprinting (4D ELF)
[56] and diffuse reflectance spectroscopy [57] have been able to
quantify tissue hemoglobin concentration as a surrogate biomarker
for malignancy. Further, low-coherence enhanced backscattering
spectroscopy (LEBS) [58] and angle-resolved low coherence
interferometry [59] have retrieved information regarding nano- and
micro-tissue morphology, thus providing insight to precancerous
states.
[0273] In this disclosure, another exemplary application of an
emerging optical technique, namely Fourier domain low coherence
interferometry (fLCI), to measure early CRC changes using an
analysis of ex-vivo tissues drawn from the azoxymethane (AOM) rat
carcinogenesis model. fLCI measures oscillatory features in depth
resolved spectra, also known as local oscillations, which result
from coherent fields induced by the scattering by the front and
back surfaces of cell nuclei in tissue [60]. Thus, fLCI uses
nuclear morphology as a biomarker of disease, making it sensitive
to the earliest stages of precancerous development. To achieve
depth resolved spectroscopic analysis, the dual window (DW)
techniques described herein can be employed, which obtain
simultaneously high spectral and depth resolution, and yield access
to the local oscillations [61]. Further, fLCI signals can be
processed to yield cross sectional images of samples, as in Fourier
domain optical coherence tomography (FD-OCT) [62], thereby enabling
co-registration of the structural information with the
spectroscopic analysis. The capabilities of fLCI using the DW
technique have been demonstrated using scattering phantoms [63] and
ex-vivo samples from a hamster cheek pouch model [60]. Here in this
example, fLCI is used to provide a spatially resolved, functional
analysis of ex-vivo tissue samples at three depths and along two
different sections of the left colon to demonstrate fLCI's ability
to detect early CRC development.
Materials and Methods
Animal Model
[0274] This study used the AOM rat carcinogenesis model, a well
characterized and established model for colon cancer research and
drug development [64]. The cancerous progression of this model is
similar to that seen in humans and is a good surrogate for human
colon cancer development. In addition, the short induction period
and high incidence of aberrant crypt foci (ACF), which are
preneoplastic lesions [65], make this model a practical choice for
testing the ability of fLCI to detect precancerous development in
the colon.
[0275] All animal experimental protocols were approved by the
Institutional Animal Care and Use Committee of The Hamner Institute
and Duke University. Forty F344 rats (six-week old, male; Charles
River Laboratories Inc., Kingston, N.Y.) were housed in The
Hamner's animal facility for a 10-day acclimation period prior to
any testing. All the animals were provided with a regular National
Institutes of Health-07 diet (Ziegler Brothers, Gardners, Pa.) for
the first 4 days of acclimation. Thereafter, the diet was switched
to the pellet form of American Institute of Nutrition (AIN)-76A
(Dyets Inc., Bethlehem, Pa.) and continued for the rest of study
period. Two animals per cage were housed in polycarbonate,
solid-bottom cages with Alpha-dry bedding in an animal room with a
12-hr light/dark cycle. Cages were changed twice a week. Pelleted,
semipurified AIN-76A diet and water were available ad libitum.
Weekly body weights were collected during the whole study period,
and clinical observations were performed to monitor the health of
the animals.
[0276] After 10 days of acclimation, the 40 rats were randomized
into groups of 10. Thirty animals received intraperitoneal (IP)
injections of AOM>90% pure with a molar concentration of 13.4 M
(Sigma, St. Louis Mo.) at a dose level of 15 mg/kg body weight,
once per week, for 2 consecutive weeks (2 doses per animal). The
remaining ten animals received saline by IP and served as the
control group. At 4, 8, and 12 weeks after the completion of the
dosing regimen, the animals (10 AOM-treated and 3 or 4
saline-treated rats per time point) were sacrificed by CO2
asphyxiation. The colon tissues were harvested, opened
longitudinally, and washed with saline. Then, the tissues were
split into 4-5 different segments, each with a length of 3-4 cm.
Only the two most distal segments of the colon were analyzed for
these experiments: the distal left colon (LC) and proximal LC.
Then, the samples were placed on a cover glass for examination with
the parallel frequency domain OCT system as described above.
Finally, the tissue samples were fixed in formalin and stained with
methylene blue in order to be scored based on the number of ACF,
which are defined as foci containing more than two aberrant crypts.
FIG. 34 shows an image 1370 of an exemplary stained tissue sample,
four (4) weeks post treatment with three ACF that contain "2," "3,"
and "4" aberrant crypts.
Detection System
[0277] FIG. 35 illustrates an exemplary parallel frequency domain
OCT system 1372 operating in scatter mode. The exemplary system
1372 used is a parallel frequency domain OCT (pfdOCT) system [66],
which consists of a Michelson interferometer geometry with the
addition of four lenses that form a 4-F interferometer [67]. Using
lenses L2 and L3 1374, 1384 as seen in FIG. 35, the multimode
fiber-coupled light from a Xe-arc lamp 1378 (e.g., 150 W, Newport
Oriel, Stratford, Conn.) is collimated onto a sample 1380. The
samples 1380 are placed atop a #0 cover glass 1382, which is tilted
slightly to avoid saturation from specular reflecti by the
glass-air interface and thus allowing detection of only the
scattered light. This is known as scatter mode imaging. For the
ex-vivo colon tissue, the lumen side was placed facing down
(against the cover glass 1382), since the light illuminates from
below the sample as seen in the inset of FIG. 35. Then, using
lenses L3 and L5, 1384, 1386, light scattered from the sample 1380
is imaged onto an entrance slit 1388 of an imaging spectrograph
1390 (e.g., SP2156, Princeton Instruments, Trenton, N.J.). A
reference arm 1392 follows a similar optical path, with lenses L2
and L4, 1374, 1376, and lenses L4 and L5 1376, 1386. After light is
dispersed into its wavelength components by the imaging spectograph
1390, the interference between the sample and reference fields is
recorded using a CCD camera (e.g., Pixis 400, Princeton
Instruments, Trenton, N.J.). Detection is centered about 600 nm
with a bandwidth of 240 nm. This configuration allows for 201
interferograms to be collected simultaneously (limited by the beam
width), yielding B-mode OCT images from a single exposure.
[0278] For this particular configuration, the system 1372 underwent
slight modifications compared to previous system implementations
reported in [60,63,66]. First, a 2.times. magnification of the
sample field at the spectrometer slit was achieved by setting the
focal length of lenses L3 and L4 1384, 1376 equal to 50 mm, and
that of lenses L2 and L5 1374, 1386 equal to 100 mm; with a pixel
size of 20 .mu.m, this resulted in a lateral resolution of 10
.mu.m. The use of shorter focal length lenses also allowed for the
total footprint of the system to be reduced, ultimately allowing
the system to be made portable. Portability is achieved by placing
the system inside an 8''.times.18''.times.24'' custom made aluminum
alloy box atop a heavy-duty stainless steel utility cart for
transportation to on-site analysis of tissue samples.
Data Processing
[0279] The fLCI process for assessing cell nuclei diameter involves
multiple steps in this example. The first step is to obtain OCT
images of the samples. Next, spatially resolved spectra are
calculated using the DW technique. Then, the spatial information
provided by the OCT images is used to co-register the spectroscopic
information; this allows for the spectra to be consistently
analyzed at specific tissue depths. Finally, spectra from specific
regions within the tissues are averaged to yield spectral
oscillations that reveal cell nuclear diameters. In this section, a
detailed exemplary procedure of these steps is provided.
[0280] To obtain OCT images in this example, the initial step is to
digitally remove the DC background from the interferometric signal
using separate acquisitions of the sample arm, reference arm, and
dark signal. Then, the interferometric data are normalized by the
reference arm intensity to remove any spectral dependence
originating from the source and detector efficiency. The
interferograms are then resampled from wavelength to a linear
wavenumber vector (k=2.pi./.lamda.), and digitally corrected for
chromatic dispersion [68]. Subsequently, the signals are Fourier
transformed to obtain OCT images with an axial resolution of
.about.1.10 .mu.m (experimental). A refractive index (RI) of n=1.38
is used to convert the optical path length to physical axial
distance in tissue [69]. FIG. 36 illustrates an exemplary
representative image 1400 of an ex-vivo rat colon sample.
[0281] To obtain depth-resolved spectroscopic information, the DW
technique is used [61]. As previously illustrated in FIGS. 5A-5C,
the method consists of multiplying two STFTs 500, 502 in FIG. 5A
that operate on each interferogram. An STFT is implemented by
sweeping a window across the interferometric data in FIG. 5A while
simultaneously taking a Fourier transform at each step, thus giving
a map of the spectral content confined within a spatial (or axial)
region, as illustrated in FIG. 5B. These maps are known as
time-frequency distributions (TFDs). However, TFDs obtained using a
single STFT suffer from an inherent trade-off between the resulting
spectral and spatial resolutions. The DW technique, on the other
hand, utilizes the high spectral resolution of an STFT using a
narrow window, and the high spatial resolution of an STFT using a
wide window to avoid the deleterious effects of the time-frequency
trade-off [61]. Here, Gaussian windows were used with standard
deviations w1=0.029 .mu.m-1 and w2=0.804 .mu.m-1, resulting in TFDs
with an axial resolution of 3.45 .mu.m and spectral resolution of
1.66 nm. Note that this process is conducted for each A-scan, thus
giving a spectrum for each point in an OCT image.
[0282] The last step to obtaining spectral information from
specific tissue depths (i.e., local oscillations) is to co-register
the OCT images 508, 510 in FIG. 5B with the DW TFDs. This process
involves using the images to identify the contour of the tissue
surfaces and calibrate the analysis relative to this "zero" depth.
Note that if a surface is not clearly discernable at any particular
A-scan, no further analysis is conducted there. With this
information, the DW TFDs can be properly aligned and thus
consistently provide spectral information from specific tissue
depths.
[0283] Two STFTs, 508, 510 in FIG. 5B, one obtained with a narrow
window and another with a wide window, are multiplied together to
obtain the DW TFD 512 in FIG. 5C. Gaussian windows were used with
standard deviations w1=0.029 .mu.m-1 and w2=0.804 .mu.m-1,
resulting in TFDs with an axial resolution of 3.45 .mu.m and
spectral resolution of 1.66 nm.
[0284] Once the spectra are properly aligned, regions of interest,
both laterally and axially, are identified and averaged in order to
provide sufficient signal-to-noise ratio for the spectral analysis
that follows. In the lateral direction, twenty (20) DW TFDs are
averaged to yield ten (10) different lateral segments in each OCT
image. Note that in previous studies all TFDs in an image were
averaged [60]; thus, the analysis provided here produces a ten-fold
increase of the spatial information. In the axial direction, the
spectral averages of 25 .mu.m depth segments from three different
sections are calculated: at the surface (surface section 0-25
.mu.m), centered about 35 .mu.m in depth (mid section. 22.5-47.5
.mu.m), and centered about 50 .mu.m in depth (low section 37.5-62.5
.mu.m). The area inside the red dotted line in FIG. 36 gives an
example of a resulting mid section from which the spectra are
averaged to determine the nuclear diameter.
[0285] The spectra from the averaged regions contain two
components. The first component contains the low frequency
oscillations that have been associated with the periodic fine
structures induced by spherical scatterers, which have been
analyzed previously using the van de Hulst approximation in light
scattering spectroscopy (LSS) [63, 70-72]. The approximation gives
an analytical solution to the scattering cross section of spherical
scatterers, which shows that the periodicity of the spectral
oscillations depends on size, as well as on the ratio between the
RI of the scatterer and surrounding medium [72]. This ultimately
results in relatively low frequency oscillations. However, it has
been observed that due to the lack of knowledge of the precise RI
of the scatterer and the surrounding medium [73], the amount of
useful information that can be extracted from the LSS method is
limited. Therefore, the low frequency oscillations are isolated
using a smoothing function in Matlab (Mathworks, Natick, Mass.) and
subsequently removed from the spectra. This process isolates the
second component: the high frequency oscillations of the spectra,
which correspond to the local oscillations resulting from coherent
fields induced by the cell nuclei in the averaged region. Unlike
the periodic fine structures in LSS, the local oscillations only
depend on the size and RI of the scatterer, thus resulting in
higher frequencies. Specifically, the periodicity of the local
oscillations is given by the sample field's round trip optical path
length (.DELTA.OPL) thought the scatterer, and is related to the
scatter size (in this case, dc) by dc=.DELTA.OPL/(2nn), where nn is
the RI of the cell nuclei. FIG. 37A illustrates the average
spectrum 1402 (solid blue line) along with the isolated low
frequency component (dotted black line) for the averaged region
shown in FIG. 36. FIG. 37B shows the resulting local oscillations
1404.
[0286] Finally, a Fourier transform of the local spectral
oscillations is taken to produce a correlation function, where it
is attributed that the peak in this function to indicate the
average cell nuclear diameter in the region of analysis [60]. Other
scatterers, such as other cellular organelles and nuclear content,
may also produce peaks in this function, but due to their random
orientation, size, and spacing with one another, the resulting
signal is unlikely to produce a peak greater in magnitude than that
of the average cell nuclear diameter. A correlation function 1406
for the local oscillations in FIG. 37B is shown in FIG. 37C, where
the correlation distance (dc) has been properly scaled to account
for the round trip optical path length and the RI of the cell
nuclei. A constant nuclear RI of nn=1.395 was assumed for this
analysis [69]. As a last step, the peak detection process is
automated to enable analysis of large data sets. To achieve this,
the correlation function is subject to further processing, where
the l/f noise is removed using a smoothing function. Then, only
maxima that are 3.5 standard deviations above the mean of the
correlation function are considered to be clear peaks. If this
criterion is not met at any particular region, the measurement is
discarded.
Results
Depth Sections
[0287] The nuclear diameters from the three different tissue depth
sections and for all time points are summarized in FIGS. 38A-38C
and Table 1408 in FIG. 39. Note that the control group measurements
of all the time points were combined, since no statistically
significant differences were found between them. Statistical tests
were conducted using a two-sided student t-test.
[0288] As shown in FIGS. 38A-38C, the mid section (35 .mu.m depth)
provided the most significant results, where the treated groups at
all three time points yielded p-values <10-4 ** when compared to
the control group. The fLCI measurement for the control group at
the mid section yielded an average cell nuclear diameter of
5.15+/-0.05 .mu.m, while for the treated groups it was found to be
5.91+/-0.15 .mu.m, 6.02+/-0.18 .mu.m, and 6.49+/-0.49 .mu.m at 4,
8, and 12 weeks after treatment, respectively. For the deepest
(low, 50 .mu.m depth) section, mildly statistically significant
results were observed, with p-values<0.05*. No statistical
significance was found at the surface, and mildly significant
differences (p-values<0.05*) were found at the low (50 .mu.m)
section.
Length Segments
[0289] The two tissue segments (proximal and distal left colon)
were further analyzed separately for the mid depth section. The
measured cell nuclear diameters and number of ACF are summarized in
Table 1410 in FIG. 40. It was found that for all the time points,
and for both segments, the measured nuclear diameters for the
treated groups were significantly different from the control group
(p-values <10-4**).
[0290] The results are also summarized in FIGS. 41A and 41B. Note
that significant differences were observed for both segments after
only four (4) weeks post treatment in this example. The measured
increase in the nuclear diameter, however, remained relatively
constant thereafter, with the exception of the last time point in
the proximal LC. Here, the nuclear diameter increased dramatically
from .about.6.0 .mu.m to .about.7.2 .mu.m. To investigate this
further, FIG. 41C plots the nuclear diameter as a function of the
average number of ACF, which are preneoplastic lesions. For
clarity, each point with its corresponding time period is also
identified. Note that the formation of ACF was faster in the
proximal LC compared to the distal LC, and that the plot shows a
region of little nuclear morphological change after the initial
formation of ACF. This plateau region is present in both sections
and is initially independent of the number of ACF. However, once
the number of ACF increased to the maximum value observed in this
study (.about.70), the measured increase of the nuclear diameter
was specific to the region manifesting more advanced neoplastic
development, in contrast to the ubiquitous and relatively constant
cell nuclear diameter measurements of the plateau region.
[0291] The results highlight the importance of obtaining spatially
resolved information for assessing tissue health. Other optical
methods have also demonstrated the need for depth selectivity, but
the specific depth that provides the most diagnostic information
has varied. Using LEBS, which assesses changes in tissue
nano-architecture, it was found that a penetration depth of 70
.mu.m yielded the most significant results [58]; whereas using 40
ELF to measure hemoglobin concentrations, a penetration depth of
100 .mu.m was found to yield significant results [56]. With these
optical methods, however, useful information is obtained by
integrating to a particular depth, rather than sampling specific
locations, which may explain the differences. In contrast, fLCI is
an interferometric technique that uses a broadband source, and thus
enables the coherence gating imaging capabilities of OCT and allows
sampling of specific points in three-dimensional space. Image
guidance was vital in this study in order to identify the tissue
surface and probe specific tissue depths.
[0292] Along with the imaging capabilities of fLCI, the DW
technique is an equally important feature to enable this study. The
DW technique avoids the spectral and spatial resolution trade-off
that has hindered quantification using STFTs or continuous wavelet
transforms. Acquisition of the local oscillations necessitates high
resolution in both dimensions, otherwise the phenomenon of fringe
washout, resulting from phase and frequency differences from
different scattering nuclei, would obscure the local oscillations
from which the cell nuclear diameter is assessed.
[0293] The results were analyzed by segments along the length of
the colon. Here, fLCI detected significant changes in segments and
at time points that presented early evidence of preneoplastic
development, underscoring the sensitivity of the method. Further,
the measured early nuclear morphological change was observed in
both segments and independently of the number of ACF, which
suggests a ubiquitous micromorphological change of the colon. This,
however, was not the case when neoplastic development became more
advanced (demarcated by the high number ACF); at which point, the
nuclear diameter increase was specific to the affected region.
These sets of results present significant findings. First, these
results suggest that fLCI may be able to detect the "field effect"
of carcinogenesis. This phenomenon describes observations that
neoplastic development in one part of the colon distorts nano- and
micro-tissue morphology, as well as tissue function, along the
entire organ. This has been a subject of much interest since it
indicates that adequate screening may be achieved by only probing
certain (and more readily accessible) sections of the colon [56,
58, 74]. These results also indicate that fLCI can identify
specific regions where more advanced neoplastic development has
occurred, which is paramount for detecting CRC development and
initiating a localized therapy.
[0294] While the results presented here are very promising, there
are certain limitations that still need to be explored in order to
take advantage of all the information provided by the method. As
described above, the procedure for obtaining fLCI measurements
assumes a constant RI value for the cell nuclei, and a different
constant value for the bulk tissue; however, it is known that the
RI can vary depending on tissue type and tissue health. Thus, these
variations, which are currently not assessed with our method, may
be introducing an additional degree of uncertainty in the
calculated nuclear diameters. Further, these variations have
hindered our ability to use the low frequency oscillations with
LSS, as previously performed using tissue phantoms [63]. However,
it is believed that a more rigorous treatment of the LSS fitting
algorithm may provide insight to the variations of the RI in future
analyses.
[0295] In this study, an AOM-treated rat model was used to
demonstrate the ability of fLCI to quantitatively distinguish
between ex-vivo colon tissue that is normal and that which exhibits
early precancerous development. The results show highly
statistically significant differences between the AOM-treated and
control group samples. Further, the results suggest that fLCI may
be able to detect changes due to the field effect of
carcinogenesis, in addition to identifying areas where more
advanced neoplastic development has occurred. Future work will be
directed towards developing an optical fiber based pfdOCT system to
demonstrate non-invasive, in-vivo early CRC detection.
[0296] FIG. 42 is a schematic diagram representation of an
exemplary machine 1420 in the exemplary form of an exemplary
computer system 1422 adapted to execute instructions from an
exemplary computer-readable medium to perform the functions of the
DW techniques described herein according to one embodiment. The
machine 1420 may be interfaced, for example, to the spectrographs
described herein to receive scattering interference term
information containing depth-resolved spectral information about a
sample. In this regard, the machine 1420 may comprise the computer
system 1422 within which a set of instructions for causing the
machine 1420 to perform any one or more of the methodologies
discussed herein may be executed. The machine 1420 may be connected
(e.g., networked) to other machines in a local area network (LAN),
an intranet, an extranet, or the Internet. The machine 1420 may
operate in a client-server network environment, or as a peer
machine in a peer-to-peer (or distributed) network environment.
While only a single machine 1420 is illustrated, the term "machine"
shall also be taken to include any collection of machines that
individually or jointly execute a set (or multiple sets) of
instructions to perform any one or more of the methodologies
discussed herein. The machine 1420 may be a server, a personal
computer, a desktop computer, a laptop computer, a personal digital
assistant (PDA), a computing pad, a mobile device, or any other
device and may represent, for example, a server or a user's
computer.
[0297] The exemplary computer system 1422 includes a processing
device or processor 1424, a main memory 1426 (e.g., read-only
memory (ROM), flash memory, dynamic random access memory (DRAM)
such as synchronous DRAM (SDRAM), etc.), and a static memory 1428
(e.g., flash memory, static random access memory (SRAM), etc.),
which may communicate with each other via a bus 1430.
Alternatively, the processing device 1424 may be connected to the
main memory 1426 and/or static memory 1428 directly or via some
other connectivity means.
[0298] The processing device 1424 represents one or more
general-purpose processing devices such as a microprocessor,
central processing unit, or the like. More particularly, the
processing device 1424 may be a complex instruction set computing
(CISC) microprocessor, a reduced instruction set computing (RISC)
microprocessor, a very long instruction word (VLIW) microprocessor,
a processor implementing other instruction sets, or processors
implementing a combination of instruction sets. The processing
device 1424 is configured to execute processing logic in
instructions 1432 for performing the operations and steps discussed
herein.
[0299] The computer system 1422 may further include a network
interface device 1434. It also may or may not include an input 1436
to receive input and selections to be communicated to the computer
system 1422 when executing instructions. It also may or may not
include an output 1438, including but not limited to a display, a
video display unit (e.g., a liquid crystal display (LCD) or a
cathode ray tube (CRT)), an alphanumeric input device (e.g., a
keyboard), and/or a cursor control device (e.g., a mouse).
[0300] The computer system 1422 may or may not include a data
storage device that includes an analysis or FPE tool 1440 stored in
a machine-accessible storage or computer-readable medium 1442 on
which is stored one or more sets of instructions 1444 (e.g.,
software) embodying any one or more of the methodologies or
functions described herein. The instructions 1444 may also reside,
completely or at least partially, within the main memory 1426
and/or within the processing device 1424 during execution thereof
by the computer system 1422, the main memory 1426 and the
processing device 1424 also constituting machine-accessible storage
media. The instructions 1444 may further be transmitted or received
over a network 1446 via the network interface device 1434.
[0301] While the machine-accessible storage medium 1442 is shown in
an exemplary embodiment to be a single medium, the term
"machine-accessible storage medium" should be taken to include a
single medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) that store the one
or more sets of instructions. The term "machine-accessible storage
medium" shall also be taken to include any medium that is capable
of storing, encoding or carrying a set of instructions for
execution by the machine and that cause the machine to perform any
one or more of the methodologies of the embodiments disclosed
herein. The term "machine-accessible storage medium" shall
accordingly be taken to include, but not be limited to, solid-state
memories, optical and magnetic media, and carrier wave signals.
[0302] In another embodiment of multiple widow signal processing,
spectroscopic optical coherence tomography (SOCT) may be used to
show molecular imaging of endogenous and exogenous chromophores,
allowing high spectral fidelity while producing in vivo tomographic
imaging at the micron scale in three dimensions. SOCT combines the
non-invasive, 3-dimensional (3-D), high resolution, imaging
capabilities of OCT with the rich source of knowledge available
with spectroscopy by leveraging the wide spectral bandwidth of low
coherent light sources required for depth sectioning via the
coherence gating process. SOCT uses the same data acquired for
conventional OCT imaging, but provides spectral information at each
voxel of the sampled volume, revealing the wavelength-dependent
attenuation of light traversed through the biological medium.
[0303] A parallel Fourier Domain OCT (pfdOCT) system having a
visible spectrum light source is shown in FIG. 43 and is generally
designated at 1500. Light from a supercontinuum laser source 1502
is filtered and sent to the pfdOCT system 1500. A cylindrical lens
1504 is used to deliver a line source onto the sample 1506, and the
scattered light from the sample and the reflected light from a
reference arm 1508 are imaged onto the entrance slit of an imaging
spectrograph 1510. A 1-D Fourier transform of the resampled data
(from wavelength to wavenumber) yields an OCT image 1512
(conventional processing). The dual window (DW) method is applied
to each interferogram, thus providing a spectrum for each point of
the corresponding A-scan. The processed spectral information may be
analyzed quantitatively or used to assign red, green, and blue
channels for true color representation of samples. The procedure is
repeated for each interferogram to obtain spatially resolved
spectroscopic information throughout the sampled volume.
[0304] With a center wavelength of 575 nm and a bandwidth of 240
nm, the light source enables molecular imaging of a wide range of
absorbers, including hemoglobin and commercially available contrast
agents (e.g., nanoparticles, methylene blue, and fluorescein). By
employing the visible region of the spectrum, much higher degrees
of absorption from Hb are observed compared to the spectral regions
more commonly used in OCT and SOCT, typically around 800 nm and
above. Signal processing was accomplished using the dual window
(DW) processing method described above. In this approach two
short-time Fourier-transforms (STFT's) were computed, one using a
wide window and another using a narrow window, and then multiplied
on a point by point basis. Mathematically, this approach has been
shown to be equivalent to probing the Wigner distribution of the
sample's scattered field using two orthogonal windows that can
independently tune the spectral and spatial resolution. As a
result, the DW distribution contains high spatial resolution while
providing superior spectral fidelity compared to the linear
approach and yet avoiding artifacts associated with other bilinear
approaches.
[0305] Data was collected from a pfdOCT system having a super
continuum laser (Fianium SC-450) as a light source. The parallel
data acquisition allows sampling of up to 400 interferograms per
acquisition (limited by the CCD and beam diameter), each with a
lateral resolution of 6 .mu.m (x-dimension). As with conventional
spectral domain OCT processing, the data for each interferogram was
recorded as a function of wavelength, resampled to linear
wavenumber array, and Fourier transformed to obtain a depth
resolved profile (z-dimension) of the scattered light with a depth
resolution of 1.2 .mu.m resolution. The high depth resolution is
achieved by using a broad spectral bandwidth, which also results in
coarse spectral sampling due to the finite number of available
pixels in the detector, setting the maximum penetration depth to
500 .mu.m in air (360 .mu.m in tissue). Three dimensional data sets
were acquired by translating the sample along the y-dimension using
a motorized translational stage (resolution is 6 .mu.m). The DW
signal processing method is applied to each interferogram to
compute the depth-resolved spectra. For image display of the
spectroscopic data, the spectrum was divided into red, green, and
blue channels using the Commission Internationale d'Eclairage (CIE)
color functions, thereby providing a true color representation of
samples. For quantitative analysis, the full detailed spectrum is
available for each voxel in the image
[0306] Images were acquired from an in-vivo CD1 nu/nu normal mouse
dorsal skinfold window chamber model as described in Koehl et al.,
Clin. Exp. Metastasis 26: 329-344 (2009), incorporated herein by
reference. For imaging of endogenous contrast, the mouse was
anesthetized and the window chamber was removed.
[0307] Referring to FIG. 44, conventional OCT imaging of the data
revealed tissue structures, as seen in the top image. In the case
of the dorsal window, several histological structures are evident,
including the muscle layer, the lumen of blood vessels--including
small capillaries--and the subcutaneous layer. However, functional
information regarding the sample is not available in this
format.
[0308] By applying DW signal processing and the color functions,
the same structures were visualized with the addition of true-color
contrast, which grants access to a wealth of spectroscopic
information that was previously unattainable. The muscle layer at
the surface appeared relatively colorless due to low concentrations
of Hb; however, once light traverses through the vasculature
network beneath, a red shift was clearly observed due to higher
concentrations of Hb (bottom image of FIG. 44). It should be noted
that with large blood vessels (>100 .mu.m), most of the light is
attenuated, thereby preventing detection of signals from below and
thus creating a `shadow` effect.
[0309] The full potential impact of this imaging method can be seen
in the en-face image, presented in FIG. 45. Here, several x-y
planes were summed to incorporate vessels found at different
depths. This image shows two major vessels, where the larger one on
the right corresponds to a vein and the other corresponds to an
artery. Several branching vessels can also be observed, along with
the capillary plexus, which is more readily appreciable in the 3-D
volume.
[0310] The true color SOCT technique provides the ability to
quantitatively analyze spatially resolved spectra from voxels
within the 3-D volume. To illustrate, four points of interest were
selected and the selected spectra analyzed; these points are found
along a branching vessel from the vein (FIG. 45(b)), two from the
artery (FIGS. 45(c) and (d)), and from the capillary network (FIG.
45(e)). The measured spectral profiles are superposed with the
theoretical Hb molar extinction coefficients. The dotted portion of
the curves outlines the region used to determine SO.sub.2 levels.
All spectra were selected from depths immediately below each
corresponding vessel. From these spectra, the oxygen saturation
(SO.sub.2) was determined using a simple linear analysis that
restricts the bandwidth to a region where the oxy- and deoxy-Hb
extinction coefficients have zero correlation (520 nm-585 nm). As
FIGS. 45(b)-(e) show, the measured spectra are well fit by this
model and correspond to expected values for these tissue
locations.
[0311] As a further demonstration of the utility of this SOCT
technique, imaging using an exogenous contrast agent can be
accomplished. To illustrate, 100 .mu.L of sodium fluorescein (NaFS)
was diluted in sterile saline to 1% by weight, was introduced into
the mouse via retro orbital injection, an injection that serves as
an alternative to a tail vein injection, with the agent introduced
in the retrobulbar sinus (behind the globe of the eye). The
resulting images clearly depicted the presence of NaFS by a severe
red shift in hue for the vessels, which arises from stronger
absorption at the lower wavelengths compared to that observed with
endogenous contrast. As a result, vessels were characterized by the
red hue of NaFS in the en-face image; however, large vessels still
exhibit a strong `shadow`. Spectra from points of interest were
taken in a manner similar to that described above for the
endogenous contrast where the contributions of the three absorbing
species (NaFS, deoxy-Hb, oxy-Hb) were seen to affect the shape of
the localized spectra to varying degrees.
[0312] In these exogenous contrast images, the fluorescent light
from the NaFS may saturate the detector and produce green areas of
the image. The contrast agent (NaFS) absorbs light with maximum
efficiency around 494 nm, producing the red hue of the vessels in
the true color SOCT image. However, it also emits incoherent
fluorescent light at a peak wavelength of 521 nm (green light),
which if particularly strong will produce the green areas and
prohibits separation of the green incoherent signal.
[0313] Many modifications and other embodiments of the embodiments
set forth herein will come to mind to one skilled in the art to
which the embodiments pertain having the benefit of the teachings
presented in the foregoing descriptions and the associated
drawings. The present disclosure is not limited to dual windows.
Multiple windows having more than two windows may be employed if
desired. The present disclosure is not limited to particular
properties of returned light from the sample. These optical
properties can include scattering properties, reflectance
properties, and absorption properties. Therefore, it is to be
understood that the description and claims are not to be limited to
the specific embodiments disclosed and that modifications and other
embodiments are intended to be included within the scope of the
described embodiments. Although specific terms are employed herein,
they are used in a generic and descriptive sense only and not for
purposes of limitation.
* * * * *