U.S. patent application number 13/542898 was filed with the patent office on 2012-11-08 for determination of a measure of a glycation end-product or disease state using tissue fluorescence lifetime.
Invention is credited to Marwood Neal Ediger, John D. Maynard.
Application Number | 20120283531 13/542898 |
Document ID | / |
Family ID | 39318840 |
Filed Date | 2012-11-08 |
United States Patent
Application |
20120283531 |
Kind Code |
A1 |
Maynard; John D. ; et
al. |
November 8, 2012 |
Determination of a Measure of a Glycation End-Product or Disease
State Using Tissue Fluorescence Lifetime
Abstract
The present invention provides methods and apparatuses for
determining a measure of a tissue state in an individual. Light
emitted by tissue of the individual due to fluorescence of a
chemical with the tissue detected. The invention can comprise
measuring the fluorescence lifetime in either time-domain or
frequency domain modes. The invention can also comprise a variety
of models relating fluorescence to a measure of tissue state, for
example, multivariate models can be developed that relate lifetime
trends of one or more constituents to increasing propensity to
diabetes and pre-diabetes. Other biologic information can be used
in combination with the fluorescence properties to aid in the
determination of a measure of tissue state.
Inventors: |
Maynard; John D.;
(Albuquerque, NM) ; Ediger; Marwood Neal;
(Albuquerque, NM) |
Family ID: |
39318840 |
Appl. No.: |
13/542898 |
Filed: |
July 6, 2012 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
11960260 |
Dec 19, 2007 |
8238993 |
|
|
13542898 |
|
|
|
|
11561380 |
Nov 17, 2006 |
8078243 |
|
|
11960260 |
|
|
|
|
10972173 |
Oct 22, 2004 |
7139598 |
|
|
11561380 |
|
|
|
|
10116272 |
Apr 4, 2002 |
7043288 |
|
|
10972173 |
|
|
|
|
60515343 |
Oct 28, 2003 |
|
|
|
60517418 |
Nov 4, 2003 |
|
|
|
Current U.S.
Class: |
600/316 |
Current CPC
Class: |
A61B 5/0059 20130101;
G01N 2021/6417 20130101; A61B 5/0071 20130101; A61B 5/412 20130101;
G01N 21/6408 20130101 |
Class at
Publication: |
600/316 |
International
Class: |
A61B 5/1455 20060101
A61B005/1455 |
Claims
1. A method of determining a tissue state of the tissue of an
individual, where "tissue state" is any of (i) a disease state
based on long-term changes (greater than one month duration) in
tissue, (ii) a measure of chemical change based on long-term
(greater than one month duration) glycemic control, (iii) a measure
of glycation end-products in tissue, and (iv) a combination of the
above, comprising: (a) illuminating a portion of the tissue of the
individual with excitation light; (b) detecting light emitted from
the tissue by fluorescence lifetime of a chemical within the
tissue, where the tissue is not the lens of the eye; (c)
determining a first tissue reflectance characteristic at an
excitation light wavelength, and a second tissue reflectance
characteristic at a detection light wavelength; (d) detecting light
returned from the tissue at the detection wavelength in response to
illumination at the excitation wavelength; (e) determining a
corrected fluorescence lifetime from the detected light and the
first tissue reflectance characteristic and the second tissue
reflectance characteristic; and (f) determining the tissue state
from the corrected fluorescence lifetime and a model relating
fluorescence lifetime and tissue state.
2. A method as in claim 1, wherein the excitation light has a
wavelength in the range from 280 nm to 500 nm.
3. A method as in claim 2, wherein the excitation light has a
wavelength in the range from 315 nm to 500 nm.
4. A method as in claim 1, wherein detecting light emitted from the
tissue comprises detecting light at a wavelength between 280 nm and
850 nm.
5. A method as in claim 1, wherein the fluorescence lifetime is
measured in a time-domain mode.
6. A method as in claim 5, wherein the time-domain mode measurement
comprises illuminating the portion of the tissue with a short pulse
of the excitation light and recording the time signature of the
fluorescence decay of the chemical within the tissue.
7. A method as in claim 1, wherein the fluorescence lifetime is
measured in a frequency-domain mode.
8. A method as in claim 7, wherein the frequency-domain mode
measurement comprises illuminating the portion of the tissue with a
modulation of the excitation light and recording the amplitude and
phase shift of the fluorescence relative to the excitation
modulation of the chemical within the tissue.
9. A method as in claim 1, wherein detecting light comprises
determining a relationship between a pulsed or modulated excitation
light illumination and fluorescence lifetime at a detection
wavelength, and wherein determining tissue state comprises
comparing the relationship with a model defining a relationship
between tissue state and relationships between the excitation light
pulse or modulation and fluorescence lifetime at the detection
wavelength.
10. A method as in claim 1, further comprising acquiring biologic
information relating to the individual, and wherein determining a
tissue state comprises determining the tissue state from the
information, the detected light, and a model relating biologic
information, fluorescence lifetime and tissue state.
11. A method as in claim 1, wherein the tissue comprises the skin
of the individual.
12. A method as in claim 1, wherein the model is determined
according to: (a) for each of a plurality of subjects: (i)
determining a fluorescence lifetime of a portion of the tissue of
the subject; and (ii) determining a tissue state of the subject;
and (b) applying a multivariate method to the plurality of
fluorescence lifetime determinations and associated tissue state
determinations to generate a model relating fluorescence lifetime
to a tissue state.
13. A method of determining a model relating fluorescence and
tissue state, where "tissue state" is any of (i) a disease state
based on long-term changes (greater than one month duration) in
tissue, (ii) a measure of chemical change based on long-term
(greater than one month duration) glycemic control, (iii) a measure
of glycation end-products in tissue, and (iv) a combination of the
above, comprising: (a) for each of a plurality of subjects: (i)
determining a fluorescence lifetime of a portion of the tissue of
the subject, where the tissue is not the lens of the eye, by
detecting light emitted from the tissue by: (1) determining a first
tissue reflectance characteristic at an excitation light
wavelength, and a second tissue reflectance characteristic at a
detection light wavelength; (2) detecting light returned from the
tissue at the detection wavelength in response to illumination at
the excitation wavelength; (3) determining a corrected fluorescence
lifetime from the detected light and the first tissue reflectance
characteristic and the second tissue reflectance characteristic;
(ii) determining a tissue state of the subject; and (b) applying a
multivariate method to the plurality of corrected fluorescence
lifetime determinations and associated tissue state determinations
to generate a model relating corrected fluorescence lifetime to
tissue state.
14. A method as in claim 13, wherein determining a fluorescence
lifetime of a portion of the tissue of the subject comprises: a.
Illuminating the portion of the tissue of the individual with
pulsed or modulated excitation light; and b. Detecting light
emitted from the tissue by fluorescence lifetime of a chemical
within the tissue.
15. A method as in claim 13, wherein determining a tissue state
comprises at least one of: (a) evaluating the subject according to
an OGTT; (b) evaluating the subject according to an FPG; (c)
evaluating the subject according to an HbA1c test; (d) determining
a previous disease state determination from disease state declared
by the subject; (e) determining a level of glycation endproducts in
the tissue of the subject.
16. A method as in claim 13, wherein applying a multivariate method
comprises applying a multivariate model constructed according to
the Partial Least Squares, Principal Components Regression,
Principal Components Analysis, Classical Least Squares, Multiple
Linear Regression, Ridge Regression algorithms, Linear Discriminant
algorithms, Quadratic Discriminant algorithms, Logistic Regression
algorithms, or a combination thereof.
17. A method as in claim 13 wherein the portion of the tissue
comprises the skin of the subject.
18. An apparatus for the determination of a tissue state in an
individual, where "tissue state" is any of (i) a disease state
based on long-term changes (greater than one month duration) in
tissue, (ii) a measure of chemical change based on long-term
(greater than one month duration) glycemic control, (iii) a measure
of glycation end-products in tissue, and (iv) a combination of the
above, comprising: (a) an illumination subsystem; (b) a detection
subsystem; and (c) a control system, configured to use the
illumination subsystem and the detection subsystem to determine a
first tissue reflectance characteristic at an excitation light
wavelength, and a second tissue reflectance characteristic at a
detection light wavelength; and configured to use the illumination
subsystem and detection subsystem to detect light returned from the
tissue at the detection wavelength in response to illumination at
the excitation wavelength (d) an analysis subsystem, configured to
determine a corrected fluorescence lifetime from the detected light
and the first tissue reflectance characteristic and the second
tissue reflectance characteristic, and comprising a model relating
fluorescence lifetime of the skin of an individual to tissue
state.
19. An apparatus as in claim 18, further comprising an input
subsystem configured to acquire biologic information comprising one
of more of: gender of the individual, weight of the individual,
waist circumference of the individual, history of disease of the
individual's family, ethnicity, skin melanin content, smoking
history of the individual, or a combination thereof; and wherein
the model relates fluorescence lifetime of the skin of an
individual and biologic information to tissue state.
20. An apparatus as in claim 18, wherein the illumination subsystem
comprises means for illuminating with a short pulse of the
excitation light source and the detection subsystem comprises means
for measuring the fluorescence lifetime by recording the time
signature of the fluorescence decay.
Description
CROSS REFERENCES TO CO-PENDING APPLICATIONS
[0001] This application claims priority as a continuation of U.S.
patent application Ser. No. 11/960,260, filed Dec. 19, 2007, which
application was a continuation-in-part of U.S. patent application
Ser. No. 11/561,380, entitled "Determination of a Measure of a
Glycation End-Product or Disease State Using Tissue Fluorescence,"
filed Nov. 17, 2006; which is a continuation of U.S. patent
application Ser. No. 10/972,173, filed Oct. 22, 2004 and now issued
as U.S. Pat. No. 7,139,598; which was a continuation in part of
U.S. patent application Ser. No. 10/116,272, filed Apr. 4, 2002 and
now issued as U.S. Pat. No. 7,043,288. U.S. patent application Ser.
No. 10/972,173 claimed the benefit of U.S. Provisional Application
No. 60/515,343, filed Oct. 28, 2003, and U.S. Provisional
Application No. 60/517,418, filed Nov. 4, 2003. Each of the
preceding patents and applications are incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] The present invention generally relates to determination of
a tissue state from tissue fluorescence. More specifically, the
present invention relates to methods and apparatuses for
determining models that relate tissue fluorescence to a tissue
state, and for determining fluorescence properties of tissue, and
for determination of a tissue state from fluorescence properties
and from appropriate models.
BACKGROUND OF THE INVENTION
[0003] Diabetes mellitus is a major health problem in the United
States and throughout the world's developed and developing nations.
In 2002, the American Diabetes Association (ADA) estimated that
18.2 million Americans--fully 6.4% of the citizenry--were afflicted
with some form of diabetes. Of these, 90-95% suffered from type 2
diabetes, and 35%, or about 6 million individuals, were
undiagnosed. See ADA Report, Diabetes Care, 2003. The World Health
Organization (WHO) estimates that 175 million people worldwide
suffer from diabetes; type 2 diabetes also represents 90% of all
diagnoses worldwide. Unfortunately, projections indicate that this
grim situation will worsen in the next two decades. The WHO
forecasts that the total number of diabetics will double before the
year 2025. Similarly, the ADA estimates that by 2020, 8.0% of the
US population, some 25 million individuals, will have contracted
the disease. Assuming rates of detection remain static, this
portends that, in less than twenty years, three of every 100
Americans will be `silent` diabetics. It is no surprise that many
have characterized the worldwide outbreak of diabetes as
epidemic.
[0004] Diabetes has a significant impact on individual health and
the national economy. U.S. health care costs related to diabetes
exceeded $132 billion in 2002. Due to the numerous complications
that result from chronic hyperglycemia, these costs were
distributed over a wide array of health services. For example,
between 5 and 10 percent of all U.S. expenditures in the areas of
cardiovascular disease, kidney disease, endocrine and metabolic
complications, and ophthalmic disorders were attributable to
diabetes. See ADA Report, Diabetes Care, 2003. These economic and
health burdens belie the fact that most diabetes-related
complications are preventable. The landmark Diabetes Control and
Complications Trial (DCCT) established that a strict regimen of
glucose monitoring, exercise, proper diet, and insulin therapy
significantly reduced the progression of and risk for developing
diabetic complications. See DCCT Research Group, N Eng J Med, 1993.
Furthermore, the ongoing Diabetes Prevention Program (DPP) has
already demonstrated that individuals at risk for diabetes can
significantly reduce their chances of contracting the disease by
implementing lifestyle changes such a weight loss and increased
physical activity. See DPP Research Group, N Eng J Med, 2002. ADA
has recommended that health care providers begin screening of
individuals with one or more disease risk factors, observing: "If
the DPP demonstrates a reduction in the incidence of type 2
diabetes as a result of one or more of the [tested] interventions,
then more widespread screening . . . may be justified". See ADA
Position Statement, Diabetes Care, 2003.
[0005] The Fasting Plasma Glucose (FPG) test is one of two accepted
clinical standards for the diagnosis of or screening for diabetes.
See ADA Committee Report, Diabetes Care, 2003. The FPG test is a
carbohydrate metabolism test that measures plasma glucose levels
after a 12-14 hour fast. Fasting stimulates the release of the
hormone glucagon, which in turn raises plasma glucose levels. In
non-diabetic individuals, the body will produce and process insulin
to counteract the rise in glucose levels. In diabetic individuals,
plasma glucose levels remain elevated. The ADA recommends that the
FPG test be administered in the morning because afternoon tests
tend to produce lower readings. In most healthy individuals, FPG
levels will fall between 70 and 100 mg/dl. Medications, exercise,
and recent illnesses can impact the results of this test, so an
appropriate medical history should be taken before it is performed.
FPG levels of 126 mg/dl or higher indicate a need for a subsequent
retest. If the same levels are reached during the retest, a
diagnosis of diabetes mellitus is typically rendered. Results that
measure only slightly above the normal range may require further
testing, including the Oral Glucose Tolerance Test (OGTT) or a
postprandial plasma glucose test, to confirm a diabetes diagnosis.
Other conditions which can cause an elevated result include
pancreatitis, Cushing's syndrome, liver or kidney disease,
eclampsia, and other acute illnesses such as sepsis or myocardial
infarction.
[0006] Because it is easier to perform and more convenient for
patients, the FPG test is strongly recommended by the ADA and is in
more widespread use than the other accepted diagnostic standard,
the OGTT. The OGTT is the clinical gold standard for diagnosis of
diabetes despite various drawbacks. After presenting in a fasting
state, the patient is administered an oral dose of glucose solution
(75 to 100 grams of dextrose) which typically causes blood glucose
levels to rise in the first hour and return to baseline within
three hours as the body produces insulin to normalize glucose
levels. Blood glucose levels may be measured four to five times
over a 3-hour OGTT administration. On average, levels typically
peak at 160-180 mg/dl from 30 minutes to 1 hour after
administration of the oral glucose dose, and then return to fasting
levels of 140 mg/dl or less within two to three hours. Factors such
as age, weight, and race can influence results, as can recent
illnesses and certain medications. For example, older individuals
will have an upper limit increase of 1 mg/dl in glucose tolerance
for every year over age 50. Current ADA guidelines dictate a
diagnosis of diabetes if the two-hour post-load blood glucose value
is greater than 200 mg/dl on two separate OGTTs administered on
different days.
[0007] In addition to these diagnostic criteria, the ADA also
recognizes two `pre-diabetic` conditions reflecting deviations from
euglycemia that, while abnormal, are considered insufficient to
merit a diagnosis of diabetes mellitus. An individual is said to
have `Impaired Fasting Glucose` (IFG) when a single FPG test falls
between 100 and 126 mg/dl. Similarly, when the OGTT yields 2-hour
post-load glucose values between 140 and 200 mg/dl, a diagnosis of
`Impaired Glucose Tolerance` (IGT) is typically rendered. Both of
these conditions are considered risk factors for diabetes, and
IFG/IGT were used as entrance criteria in the Diabetes Prevention
Program. IFG/IGT are also associated with increased risk of
cardiovascular disease.
[0008] The need for pre-test fasting, invasive blood draws, and
repeat testing on multiple days combine to make the OGTT and FPG
tests inconvenient for the patient and expensive to administer. In
addition, the diagnostic accuracy of these tests leaves significant
room for improvement. See, e.g., M. P. Stern, et al., Ann Intern
Med, 2002, and J. S. Yudkin et al., BMJ, 1990. Various attempts
have been made in the past to avoid the disadvantages of the FPG
and OGTT in diabetes screening. For example, risk assessments based
on patient history and paper-and-pencil tests have been attempted,
but such techniques have typically resulted in lackluster
diagnostic accuracy. In addition, the use of glycated hemoglobin
(HbA1c) has been suggested for diabetes screening. However, because
HbA1c is an indicator of average glycemia over a period of several
weeks, its inherent variability combines with the experimental
uncertainty associated with currently-available HbA1c assays to
make it a rather poor indicator of diabetes. See ADA Committee
Report, Diabetes Care, 2003. HbA1c levels of diabetics can overlap
those of nondiabetics, making HbA1c problematic as a screening
test. A reliable, convenient, and cost-effective means to screen
for diabetes mellitus is needed. Also, a reliable, convenient, and
cost-effective means for measuring effects of diabetes could help
in treating the disease and avoiding complications from the
disease.
[0009] U.S. Pat. No. 5,582,168 (Samuels) discloses apparatus and
methods for measuring characteristics of biological tissues and
similar materials. These apparatus and methods are described with
respect to measurements of the human eye. In addition, the
correction methodologies described by these inventors involve only
measurements of the elastically scattered excitation light. Samuels
describes a simple linear correction technique. Samuels does not
disclose an algorithm or methods by which tissue disease status may
be discriminated via noninvasive measurements.
[0010] U.S. Pat. No. 6,505,059 (Kollias) discloses instruments and
methods for noninvasive tissue glucose level monitoring. Kollias
does not describe any method by which measured fluorescence can be
corrected for the effects of tissue absorption and scattering.
While Kollias indicates that a tissue reflectance measurement can
be made to measure tissue scattering directly, it does not indicate
how one would use this information to obtain information regarding
the tissue fluorescence spectrum. Furthermore, Kollias does not
disclose an algorithm or methods by which tissue disease status may
be determined from noninvasive measurements.
[0011] U.S. Pat. No. 6,571,118 (Utzinger) discloses methods and
apparatus for performing fluorescence and spatially resolved
reflectance spectroscopy on a sample. While Utzinger describes a
technique in which a combination of fluorescence and reflectance
measurements are used to characterize biological tissue, the
application does not relate to spectroscopy of the skin.
Furthermore, the reflectance measurements described in Utzinger are
spatially-resolved in nature, that is, the reflectance spectroscopy
is to be conducted at one or more specific source-receiver
separations. Finally, no algorithm or process is described by which
the measured fluorescence may be corrected using the tissue
reflectance measurements to obtain or approximate the intrinsic
fluorescence spectrum of the tissue in question.
[0012] US Patent application 20030013973 (Georgakoudi) discloses a
system and methods of fluorescence, reflectance and light
scattering spectroscopy for measuring tissue characteristics.
Georgakoudi discusses estimation of intrinsic fluorescence using
reflectance properties as applied to detection of esophageal cancer
and Barrett's esophagus. Georgakoudi does not describe any specific
techniques for such estimation.
[0013] U.S. Pat. No. 6,088,606 (Ignotz) discloses a system and
method for determining the duration of a medical condition. Ignotz
mentions fluorescence, but does not use a reflectance spectrum to
obtain or estimate an intrinsic fluorescence spectrum. In addition,
Ignotz described methods relating to determining the duration of a
disease, not for diagnosing or screening for the presence of
disease or for quantifying the concentration of specified chemical
analytes. Finally, Ignotz does not address skin as a useful
measurement site.
[0014] U.S. Pat. No. 5,601,079 (Wong) describes an apparatus for
the non-invasive quantification of glucose control, aging, and
advanced Maillard products by stimulated fluorescence. Wong
specifically quantifies Advanced Glycation Endproducts in the
blood, not in the skin and/or its structural proteins. In addition,
the fluorescence correction methodology involves only measurements
of the elastically scattered excitation light. Wong describes only
a simple linear correction technique. Finally, Wong does not
disclose an algorithm or methods by which tissue disease status may
be discriminated via noninvasive measurements.
[0015] International patent publication WO 01/22869 (Smits)
describes an apparatus for non-invasive determination of skin
autofluorescence. The apparatus consists of a broadband UV source
(blacklight) that illuminates skin through interchangeable optical
bandpass filters. Resulting skin fluorescence is fiber-optically
coupled to a compact spectrophotometer. The application proffers
AGE concentration in the skin can be inferred from qualitative
assessment of skin autofluorescence but it does not describe any
means by which the AGE content can be quantified using the device
and measurement techniques. The apparatus is intended to assess
skin fluorescence in healthy individuals and does not address the
utility of the device for disease determination. The application
notes that individual skin coloring and substructure can be a
measurement interferent but it is silent on techniques or methods
to compensate for these variable characteristics.
SUMMARY OF THE INVENTION
[0016] The present invention provides a method of determining
tissue state in an individual. A portion of the tissue of the
individual is illuminated with excitation light, and then light
emitted by the tissue due to fluorescence of a chemical in the
tissue responsive to the excitation light is detected. The detected
light can be combined with a model relating fluorescence with
disease state to determine a disease state of the individual. The
invention can comprise single wavelength excitation light, scanning
of excitation light (illuminating the tissue at a plurality of
wavelengths), detection at a single wavelength, scanning of
detection wavelengths (detecting emitted light at a plurality of
wavelengths), and combinations thereof. The invention can comprise
measuring the fluorescence lifetime of the tissue in response to
pulsed or modulated excitation light. The invention also can
comprise correction techniques that reduce determination errors due
to detection of light other than that from fluorescence of a
chemical in the tissue. For example, the reflectance of the tissue
can lead to errors if appropriate correction is not employed. The
invention can also comprise a variety of models relating
fluorescence to disease state, including a variety of methods for
generating such models. Other biologic information can be used in
combination with the fluorescence properties to aid in the
determination of tissue state, for example age of the individual,
height of the individual, weight of the individual, history of
disease in the individual's family, ethnicity, skin melanin
content, or a combination thereof. Raman or near-infrared
spectroscopic examination can also be used to supply additional
information, for example like that discussed in U.S. patent
application Ser. No. 10/116,272, entitled "Apparatus And Method For
Spectroscopic Analysis Of Tissue To Detect Diabetes In An
Individual," filed Apr. 4, 2002. The invention also comprises
apparatuses suitable for carrying out the method, including
appropriate light sources, tissue sampling devices, detectors, and
models (for example, implemented on computers) used to relate
detected fluorescence and disease state.
[0017] As used herein, "determining a disease state" includes
determining the presence or likelihood of diabetes; the degree of
progression of diabetes; a change in the presence, likelihood, or
progression of diabetes; a probability of having, not having,
developing, or not developing diabetes; the presence, absence,
progression, or likelihood of complications from diabetes.
"Diabetes" includes a number of blood glucose regulation
conditions, including Type I, Type II, and gestational diabetes,
other types of diabetes as recognized by the American Diabetes
Association (See ADA Committee Report, Diabetes Care, 2003),
hyperglycemia, impaired fasting glucose, impaired glucose
tolerance, and pre-diabetes. "Tissue reflectance characteristic"
includes any reflectance property of tissue that is useful in
correction of detected light, including as examples the tissue
reflectance at the fluorescence excitation wavelength, the tissue
reflectance at the fluorescence emission wavelength, and the tissue
reflectance at other wavelengths found useful for estimating the
tissue's intrinsic fluorescence spectrum. A "measure of chemical
change due to glycemic control" means any change in the chemical
characteristics of tissue that is due to glycemic control, examples
including concentration, measurements of the presence,
concentration, or change in concentration of glycation end-products
in tissue; measurements of the rate or change in the rate of the
accumulation of such end-products; measurements of tissue membrane
thickness or the change, rate of change, or direction of change of
such thickness; tissue properties such as tensile strength, strain,
or compressibility, or the change, rate of change, or direction of
change of such property. A "measure of glycation end-product" means
any measure of the presence, time, extent, or state of tissue
associated with hyperglycemia, including, as examples, measurements
of the presence, concentration, or change in concentration of
glycation end-products in tissue; measurements of the rate or
change in the rate of the accumulation of such end-products;
measurements of the presence, intensity, or change in intensity of
fluorescence at wavelengths known to be associated with tissue
glycation end-products; and measurements of the rate or change in
the rate of the accumulation of such fluorescence. "Determination
of a tissue state" comprises determination of disease state,
determination of a measure of chemical change due to glycemic
control, determination of a measure of glycation end-products in
tissue, or a combination thereof. When light is described as having
a "single wavelength", it is understood that the light can actually
comprise light at a plurality of wavelengths, but that a
significant portion of the energy in the light is transmitted at a
single wavelength or at a range of wavelengths near a single
wavelength.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The drawings, which are not necessarily to scale, depict
illustrative embodiments and are not intended to limit the scope of
the invention.
[0019] FIG. 1 is a graph of excitation spectra in which the
excitation wavelength was scanned from 315 to 385 nm while
measuring the emitted fluorescence at a fixed wavelength of 400
nm.
[0020] FIG. 2 is a graph of emission scan data in which the
excitation was fixed at 325 nm and the fluorescence was monitored
by scanning the detection sub-system from 340 to 500 nm.
[0021] FIG. 3 is a depiction of the insertion variance of the
measured (solid lines, `uncorrected`) and intrinsic-corrected
spectra (dashed lines, k=0.5, n=0.7) spectra in FIGS. 1 and 2.
[0022] FIG. 4 is a diagrammatic representation of model-building
steps typically followed when the end goal is to use the model to
assess tissue disease state.
[0023] FIG. 5 is an illustration of the manner in which a
discriminant function might find the best separation between two
groups.
[0024] FIG. 6 is an illustration of data sets and their
corresponding wavelength regions.
[0025] FIG. 7 is a box-and-whisker plot of cross-validate posterior
probabilities of membership in the diabetic class for all study
participants.
[0026] FIG. 8 is an illustration of a receiver-operator curve
associated with the present invention and a receiver-operator curve
associated with the Fasting Plasma Glucose test.
[0027] FIG. 9 is an illustration of results of a cross-validation
in which all data from a single study participant were rotated out
in each iteration.
[0028] FIG. 10 is an illustration of a receiver-operator curve
associated with the present invention and a receiver-operator curve
associated with the Fasting Plasma Glucose test.
[0029] FIG. 11 is a schematic representation of components or
sub-systems of an apparatus according to the present invention.
[0030] FIG. 12 is a depiction of an example skin fluorimeter.
[0031] FIG. 13 is a schematic depiction of a portion of an
apparatus according to the present invention.
[0032] FIG. 14 is a schematic depiction of a portion of an
apparatus according to the present invention.
[0033] FIG. 15 is an illustration of a tissue interface suitable
for use in the present invention.
[0034] FIG. 16 is a schematic depiction of a multiple-channel fiber
optic tissue probe of geometric arrangement.
[0035] FIG. 17 is a schematic depiction of a multiple-channel fiber
optic tissue probe of a circular arrangement.
[0036] FIG. 18 is a schematic depiction of a multiple-channel fiber
optic tissue probe of a linear arrangement.
[0037] FIG. 19 is a schematic depiction of a sectional view of part
of a multiple-channel fiber optic tissue probe of a vertical
arrangement.
[0038] FIG. 20 is a schematic depiction of a sectional view of part
of a multiple-channel fiber optic tissue probe of a tilted
arrangement.
[0039] FIG. 21 is a schematic depiction of a sectional view of part
of a multiple-channel fiber optic tissue probe of a tilted
arrangement.
[0040] FIG. 22 is a schematic depiction of an isometric view of a
fiber optic tissue probe.
[0041] FIG. 23 is an illustration of a multiple-channel fiber optic
tissue probe interrogating a tissue volume at various excitation
and receiver separations.
[0042] FIG. 24 is a schematic illustration of excitation (solid
line) and emission (dashed line) signals in fluorescence lifetime
acquired in the frequency domain. Annotations indicate the phase
and demodulation parameters to extract the fluorescence lifetime
via Equations 7-9.
[0043] FIG. 25 is a time resolved fluorescence emission waveform
(solid, gray line). The fluorescence lifetime is estimated by a
single exponential function (dashed line).
[0044] FIGS. 26A and 26B are block diagrams of both frequency and
time domain apparatus for measuring fluorescence lifetimes,
respectively.
DETAILED DESCRIPTION OF THE INVENTION
[0045] Exposure of proteins to glucose generally leads to
nonenzymatic glycation and glycoxidation, a process known as the
Maillard reaction. The stable endproducts of the Maillard reaction
are collectively denoted Advanced Glycation Endproducts (AGEs). In
the absence of significant clearance, these AGEs accumulate at
rates proportional to the average level of glycemia. The Maillard
reaction can be viewed as an aging process that occurs routinely in
health and at an accelerated rate in diabetics due to the presence
of chronic hyperglycemia. In skin, collagen is the most abundant
protein and readily undergoes glycation. Skin collagen AGEs
commonly take the form of fluorescent crosslinks and adducts;
pentosidine (a crosslink) and carboxymethyl-lysine (CML, an adduct)
are two well-studied examples of skin-collagen AGEs. Other examples
of AGEs include fluorolink, pyrraline, crosslines, N . . .
-(2-carboxyethyl) lysine (CEL) glyoxal-lysine dimer (GOLD),
methylglyoxal-lysine dimer (MOLD), 3DG-ARG imidazolone,
vesperlysines A, B, C, and threosidine. One common measure of
aggregate AGE production and concomitant collagen cross-linking is
the level of collagen-linked fluorescence (CLF). CLF is typically
measured in vitro by monitoring fluorescence emission of chemically
isolated collagen in the 400-500 nm region after excitation at or
near 370 nm. See Monnier, NEJM, 1986.
[0046] The relatively long half-life (t.sub.1/2.apprxeq.15 yr) of
skin collagen and the fluorescent properties of many of its
associated AGEs make these species potential indicators of
cumulative tissue glycemia. CLF intensity and levels of specific
skin AGEs are correlated with the presence and severity of
end-organ diabetes complications such as joint stiffness,
retinopathy, nephropathy, and arterial stiffness. See Buckingham,
Diabetes Care, 1984; Buckingham, J Clin Invest, 1990; Monnier, NEJM
1986; Monnier, J Clin Invest 1986; Sell, Diabetes, 1992. In the
largest such study to date, the DCCT Skin Collagen Ancillary Study
Group evaluated a number of skin collagen variables from punch
biopsies that were donated by a large fraction of the study's
participants. These researchers found that skin AGEs were
significantly correlated with the presence and clinical grade of
diabetic neuropathy, nephropathy, and retinopathy. See Monnier et
al., Diabetes, 1999.
[0047] The present invention can determine the diabetic state of a
subject using one or more noninvasive fluorescence measurements.
The invention can illuminate a portion of the tissue of the
individual (e.g., a portion of the skin) with excitation light and
detect fluorescent light emitted by the tissue. The fluorescence
measurements can include at least one set of excitation and
emission wavelengths corresponding to the CLF window described
above. The characteristics of the fluorescent light convey
information about the disease state of the tissue under
interrogation. The invention can apply additional processing
algorithms to the measured fluorescence before imposing a simple
numerical threshold or a more detailed mathematical model to relate
the optical information to disease state. In other embodiments, the
output of the thresholding process or mathematical model can be a
quantitative measure of diabetes-induced chemical change in the
tissue of the individual being measured rendered without regard to
the individual's diabetic status. In additional embodiments, the
invention can utilize a quantitative measure of diabetes-induced
chemical changes in order to further infer or classify the diabetic
status of the individual undergoing measurement.
Determining a Fluorescence Property of Tissue
[0048] Tissue fluorescence is initiated when tissue is illuminated
by light that promotes electrons in various molecular species to
excited energy levels. Some of the excited molecules decay
radiatively, emitting light as the electrons return to a lower
energy state. The remitted fluorescence is always of a longer
wavelength (lower photon energy) than that of the excitation. The
absorption and fluorescence spectra of biomolecules are typically
broad and overlapping. Most tissues will absorb a wide range of
wavelengths. For a given excitation wavelength, the remitted
fluorescence spectrum is often correspondingly broad. Several
factors impact the useful range of excitation and emission
wavelengths. The fluorescing species (e.g. pentosidine) typically
absorb most strongly in the UVA (315-400 nm) and remit in the UVA
through short wavelength visible range (340-500 nm). The long
wavelength limit of the excitation and emission range is usually
imposed by the electronic structure of the fluorescing components.
In clinical studies, the inventors have found that fluorescence
excitation over the range of 280 to 500 nm with corresponding
detection over the range of 280 to 850 nm to be useful for the
detection of disease, quantification of AGEs and determination of a
tissue state. Optical safety considerations can limit the shortest
practical excitation wavelengths to the UVA or longer wavelengths.
The threshold limit values for optical exposure decrease
dramatically for wavelengths below 315 nm. Consequently, safe
exposure times for wavelengths in the UVB (280-315 nm) can be too
brief for effective spectral data acquisition.
[0049] Only gross biochemical and morphological tissue information
can be obtained if the spectral selectivity of either the
excitation or emission sections of a fluorimeter is relatively
coarse. A more useful approach is to consider the emission at a
particular wavelength (or narrow range of wavelengths) in response
to excitation by light having a single or narrow range of
wavelengths--an excitation/emission pair. In practice, the
fluorescence signal at a particular wavelength pair can be
monitored, or signals corresponding to a collection of
excitation/emission pairs can be acquired. Emission spectra (or
emission scans) are created when the source wavelength is fixed and
fluorescence signal is acquired over a range of emission
wavelengths. Similarly, excitation spectra are acquired by fixing
the wavelength of emitted fluorescence that is detected while the
source wavelength is varied. An excitation-emission map can be used
to represent the fluorescence signal as a topographic surface
covering a range of excitation and emission wavelengths. Emission
and excitation spectra correspond to orthogonal sections of such a
map. The points falling on the diagonal of an excitation-emission
map, that is, where the excitation and emission wavelengths are
equal, indicate the intensity of elastically scattered photons that
are reflected by the tissue back to the detection system. These
`reflectance` measurements can be obtained by synchronous scanning
of both the excitation and emission monochromators in a fluorimeter
or by a separate dedicated apparatus. Both fluorescence and
reflectance measurements can be used to ascertain the true or
`intrinsic` fluorescence properties of an optically turbid medium
such a biological tissue.
[0050] When excitation light is launched into the tissue, it is
subject to scattering and absorption processes that vary with the
optical properties of the site under interrogation, the excitation
wavelength, and the optical probe geometry. Emitted fluorescent
light is also subject to wavelength- and location-dependent
absorption and scattering as it propagates through the tissue prior
to emergence and collection. Often, the tissue property of interest
is its `intrinsic` fluorescence, defined as the fluorescence
emitted by a specimen that is homogeneous, nonscattering, and
optically dilute. In order to accurately characterize the intrinsic
fluorescence spectrum of the tissue of interest, the
spectra-altering effects of scattering and absorption that are
impressed upon the excitation and emitted light can be removed.
Variations due to subject-to-subject and site-to-site differences
can overwhelm the subtle spectral variations indicative of tissue
status. Spectral correction based upon the tissue optics of each
subject (at the same site as the fluorescence measurement, or at a
different site having a predictable relationship to the site) can
reveal the intrinsic fluorescence spectra of the molecules of
interest. This intrinsic correction mitigates the variations across
and within subjects, unmasking the spectral features relating to
presence and state of disease.
[0051] The data described in this example were collected with a
SkinSkan fluorimeter (marketed by Jobin-Yvon, Edison, N.J., USA).
The excitation and emission sides of the SkinSkan system have dual
scanning 1/8-m grating monochromators, accomplishing a .about.5 nm
system bandpass. Excitation light is provided by a 100 W Xe-arc
lamp and is f/number matched to a bifurcated fiber probe containing
31 source and 31 detection fibers. The fibers have 200-micron core
diameters and are randomly arranged in a 6-mm diameter circular
bundle within a ferrule, the distal end of which serves as the skin
interface. The output ends of the detection fibers are stacked into
an input ferrule, and the fibers' width forms the entrance slit to
the first input monochromator. Optical detection is accomplished
with a photomultiplier, the gain of which can be controlled via
software. Whenever noninvasive spectroscopy was performed,
background measurements of a uniformly reflecting material (2%
Spectralon, LabSphere, North Sutton, N.H., USA) were also obtained
to facilitate removal of the instrument lineshape. In addition, the
SkinSkan system provides a silicon photodetector that independently
monitors the excitation lamp, allowing for correction for lamp
intensity fluctuations. Thus, `measured` skin fluorescence values,
F.sub.meas, are reported as:
F meas ( .lamda. x , .lamda. m ) = F tiss ( .lamda. m ) - I DC L (
.lamda. x ; t tiss ) L ( .lamda. m ; t back ) R back ( .lamda. m )
- I DC , Eq 1 ##EQU00001##
where x is the excitation wavelength, m is the emission wavelength,
F.sub.tiss is the `raw` fluorescence at the detector, I.sub.DC is
the PMT dark current, L is the excitation lamp intensity, t denotes
time, back refers to the Spectralon background, and R.sub.back is
the reflectance of the Spectralon background. Similarly, measured
skin reflectance values, R.sub.meas are reported as:
R meas ( .lamda. ) = R tiss ( .lamda. ) - I DC L ( .lamda. ; t tiss
) L ( .lamda. ; t back ) R back ( .lamda. ) - I DC Eq 2
##EQU00002##
where R.sub.tiss is the `raw` tissue reflectance signal at the
detector. When the SkinSkan system is used for both fluorescence
and reflectance measurements, it is required that a different PMT
bias voltage be used for each measurement modality in order to
avoid detector saturation.
[0052] Typical measured fluorescence spectra of skin are shown in
the left panels of FIGS. 1 and 2. These figures illustrate spectra
obtained in two different wavelength ranges under different
collection modalities. FIG. 1 shows excitation spectra in which the
excitation wavelength was scanned from 315 to 385 nm while
measuring the emitted fluorescence at a fixed wavelength of 400 nm.
FIG. 2 presents emission scan data in which the excitation was
fixed at 325 nm and the fluorescence was monitored by scanning the
detection sub-system from 340 to 500 nm. All spectra were obtained
from the volar forearms of 17 diabetic and 17 non-diabetic subjects
between the ages of 40 and 60 years. The center panel of these
figures depicts the measured reflectance spectra. Each reflectance
spectrum corresponds to a specific fluorescence spectrum and was
acquired at same site on the same subject. The fluorescence and
reflectance spectra demonstrate typical variations resulting from
imperfect probe repositioning, environmental changes and
subject-to-subject physiological differences. These variations can
exceed the spectral variations due to disease state and hamper the
diagnostic utility of the measured spectra. In order to accurately
discriminate or quantify disease state, additional tissue-specific
spectral corrections can be applied to obtain the intrinsic tissue
fluorescence. One approximation for estimating the intrinsic
fluorescence spectrum, F.sub.corr, involves dividing the measured
fluorescence spectrum by the product of the roots of the measured
reflectance at the excitation and/or emission wavelengths (see, for
example, Finlay et al., Photochem Photobiol, 2001, and Wu et al.,
Appl Opt, 1993):
Separability f = | x _ 1 , f - x _ 2 , f | s 1 , f 2 + s 2 , f 2 ,
Eq 10 ##EQU00003##
The optimum values for n and k are dependent on the arrangement of
source and detector fibers, and can be determined empirically.
Intrinsic fluorescence spectra obtained from the spectra of FIG.
1-2 using the correction function of Equation 3 with values of
k=0.5 and n=0.7, are shown in the right panels of these figures.
Note that the intrinsic correction has removed much of the
inter-patient variation, and coarse groups of spectra corresponding
to disease state can now be visually resolved.
[0053] The values of n and k used in the intrinsic corrections
illustrated in FIGS. 1 and 2 were selected in order to minimize the
spectroscopic variation associated with repeated insertions of a
study participant's forearm into the measurement device. If
multiple spectra are collected from each participant on a patient
visit, then the spectroscopic insertion variation, S.sub.insert, of
the ith spectrum for subject j can be expressed as the absolute
deviation of that spectrum from the subject's median:
S.sub.insert
i,j(.lamda.,n,k)=abs[F.sub.corr.sub.i,j(.lamda.,n,k)-median(F.sub.corr.cn-
dot.,j(.lamda.,n,k))]/median(F.sub.corr.cndot.,j(.lamda.,n,k)). Eq
4
An aggregate measure of insertion variation is then the variance of
S.sub.insert:
v.sub.insert(.lamda.,n,k)=var(S.sub.insert(.lamda.,n,k)). Eq 5
[0054] FIG. 3 depicts the insertion variance of the measured (solid
lines, `uncorrected`) and intrinsic-corrected spectra (dashed
lines, k=0.5, n=0.7) spectra in FIGS. 1 and 2. It can be seen that
the intrinsic correction process reduces the insertion variance by
approximately a factor of four over the full wavelength range.
Under the presumption that the intrinsic fluorescence of the tissue
does not change from insertion to insertion, this procedure
mitigates a portion of the corrupting effects of variation in
tissue optical properties.
[0055] A variety of other procedures can accomplish intrinsic
fluorescence correction. For example, a number of methods have been
described by which the measured fluorescence can be corrected using
knowledge of the measured reflectance, tissue optical properties,
and probe-dependent parameters. See, e.g., Gardner et al., Appl
Opt, 1996, Zhang et al., Opt Lett, 2000; Muller et al., Appl Opt,
2001. In addition, intrinsic fluorescence corrections can be made
using a procedure in which the correction parameters for a given
fluorescence probe are created by measuring one or more tissue
phantoms for which the fluorescence, absorption, and scattering
properties have been well-characterized. This procedure can also be
accomplished via Monte-Carlo or other computer simulation of the
optical probe's response to media with known optical properties.
Any of these processes can be used to correct for the effects of
tissue optical properties in noninvasive skin fluorescence
measurements. A multi-channel optical probe as described here can
enable the measurement of optical properties of the tissue. The
optical properties can be determined by solving analytic
expressions given multi-channel fluorescence and/or reflectance
measurements. Alternatively, optical properties can be estimated
from the spectroscopic measurements by comparison with look-up
tables relating measured values to predetermined optical property
values. Such look-up tables can be generated from numerical models
that simulate multi-channel intensity measurements over a range of
simulated optical properties. Look-up tables can also be
constructed from experimental measurements of tissue-like phantoms
spanning a range of optical properties. The measured or estimated
optical properties can then be applied to correct for the spectral
distortion they induce on incident and fluorescent light.
Correction can be accomplished by comparison to a probe calibration
tables that can be derived either numerically or experimentally.
Inversion algorithms of fluorescence spectroscopy can also be
applied to extract the intrinsic dermal fluorescence once measured
or estimated optical properties of the tissue have been determined.
Alternative methods for multi-channel optical correction of tissue
fluorescence include soft-model techniques such as described above
(Eq 3). A multi-channel measurement can be used to mitigate the
impact of epidermal pigmentation and superficial blood content. For
example, by taking the ratio of the reflectance measurement at
adjacent channels (Eq 6), the filtering effects of the epidermis
are essentially removed, yielding a ratio of transfer functions of
the two channels and thus the tissue layers that they
interrogate.
R.sub.1=I.sub.0exp(-.mu..sub.a,epi2t.sub.epi)T.sub.1(.mu..sub.a,derm,.mu-
..sub.s,derm),
R.sub.2=I.sub.0exp(-.mu..sub.a,epi2t.sub.epi)T.sub.2(.mu..sub.a,derm,.mu-
..sub.s,derm), Eq 6
R.sub.norm=R.sub.1/R.sub.2=T.sub.1/T.sub.2,
Applying techniques per Equation 6, to the respective channels'
fluorescence signals yields a fluorescence transfer function that
can provide useful fluorescence information with the masking
effects of the epidermis and upper dermis largely eliminated.
Spectroscopic data from individual channels can be fused and/or
combined to provide multivariate techniques additional spectral
information that may yield more accurate and/or robust
quantification and classification models.
[0056] While the examples described here generally concern
steady-state fluorescence measurements without regard to
polarization, it is possible to apply these methods to other
fluorescence measurement modalities. For example, frequency-domain
fluorescence spectroscopy, in which the excitation light is
amplitude-modulated at RF frequencies and the phase and modulation
of the emission light are monitored, can be suitable. In addition,
using polarized excitation light and polarization-sensitive
detection, it is possible to measure the fluorescence anisotropy,
defined by r=(I.sub.II-I.sub..perp.)/(I.sub.II+2 I.sub..perp.),
where I.sub.II and I.sub..perp. are the fluorescence intensities
with polarization parallel and perpendicular to that of a linearly
polarized excitation beam. Fluorescence anisotropy measurements can
separate signals from fluorophores with overlapping spectra but
different rotational correlation times or molecular
orientations.
[0057] Another suitable approach involves time-resolved techniques,
in which a short burst of excitation light is applied to the
tissue, after which the time-evolution of the resulting
fluorescence emission is sampled. Both frequency-domain and
time-resolved measurements add the capability to monitor, for
example, fluorescence lifetime, a parameter that can provide
additional discrimination power. For example, pre-cursors to
diabetes in the form of AGE changes indicative of hyperglycemic
damage to tissue can be detected by time-resolved fluorescence
spectroscopy (TRFS). Steady-state fluorescence spectroscopy
primarily characterizes AGE-related tissue changes through shape
changes. Conversely, TRFS measures the fluorescence lifetime of
tissue constituents that vary in response to conformational,
chemical and metabolic changes. By relating lifetime changes
related to diabetes and its precursors, prospective models can be
developed and embedded on TFRS-based instruments to perform
diabetes screening and monitoring.
[0058] Fluorescence lifetimes can be measured in either
frequency-domain or the time-domain modes. The sample's
fluorescence lifetime(s) can be elucidated via frequency-domain
measurements. In the frequency-domain mode, a modulated excitation
light source illuminates the sample. To extract fluorescence
lifetime in the frequency-domain mode, the phase shift and
modulation factor of the emitted fluorescence are measured. This is
can be accomplished via digital signal processing techniques which
are equivalent to digitizing the temporal profile of the emitted
fluorescence and computing the lifetime parameters. In cases of
very short lifetimes the optical excitation must be modulated at
commensurately high frequencies. In such instances, frequency
multiplication or cross-correlation techniques may be needed in
order to accurately characterize the detected fluorescence. The
modulated excitation and emission for a fluorophore with a single
lifetime is illustrated in FIG. 24. The phase lifetime is related
to the phase angle by
tan .phi.=.omega..tau..sub.p Eq 7
and the modulation lifetime is obtained from the demodulation
factor, m:
m=Ba/bA Eq 8
m= {square root over (1+.omega..sup.2.tau..sub.m.sup.2)} Eq 9
In Equation 9, .omega.=2.pi.f, where f is the excitation modulation
frequency in Hz. In mono-exponential decays,
.tau..sub.p=.tau..sub.m=.tau., the actual fluorescence lifetime. In
more complex systems exhibiting multiple exponential decays, the
phase angle and modulation factor are measured as the modulation
frequency is varied. The component lifetimes can then be extracted
from the resulting set of phase and modulation lifetimes.
[0059] In time-domain mode, the sample is excited by a short
optical pulse and the time-resolved emission is recorded. In cases
of short lifetimes, multiple pulses and techniques such as temporal
cross-correlation may be used to accurately characterize the
fluorescence decay. The time-resolved signal can then be fit by
single or multiple exponential functions, minimizing least squares
residuals, to estimate the fluorescence lifetime(s). A
time-resolved fluorescence waveform is depicted in FIG. 25. In this
case, the fluorescence decay is estimated by a single exponential
function. Alternatively, Fourier transform or deconvolution methods
can be used to extract the constituent lifetimes from the
time-resolved signal.
[0060] The relationships between disease state and fluorescence
lifetimes can be developed via univariate or multivariate
techniques. One example is to conduct clinical studies to collect
both spectral and physiological data. Calibration models can then
be developed. For instance, partial least squares can be applied to
create models relating the fluorescence lifetimes and continuous
reference values like that from the 2-hour oral glucose tolerance
test. Alternatively, classification models based upon discriminant
analysis or logistic regression can be build using the lifetime
values and the corresponding disease class (e.g., abnormal glucose
tolerance).
[0061] Representative system schematics for fluorescence lifetime
measurements are depicted in FIGS. 26A and 26B. In frequency-domain
mode, shown in FIG. 26A, a typical light source is a continuous
wave laser and modulator such as a Pockels cell, a detector
apparatus and analysis hardware and software to record and
characterize the phase shift and demodulation of the emitted
fluorescence. The analysis hardware may consist of temporal
digitizing systems, time-correlated analysis, or other techniques
to extract the lifetime character of the fluorescence. In
time-domain mode, shown in FIG. 26B, the light source is typically
a short pulse laser. In some instances, a mode-locked laser,
emitting a train of short pulses may be employed. The detection
system can work strictly in time-resolved mode, recording the
temporal profile of the fluorescence waveform and then extracting
the estimated fluorescence lifetime(s). Alternatively, with a
repetitively pulsed light source (e.g., mode-locked laser) the
temporal correlation methods can be used to determine the
fluorescence lifetime.
[0062] In addition, any of these techniques can be used in
conjunction with an imaging methodology such as microscopy or
macroscopic scanning of the excitation beam in order to acquire
information about the spatial distribution of fluorophores. Any of
the above-mentioned methods can be used in conjunction with a
measurement technique that allows depth discrimination, such as a
confocal detection system or optical coherence tomography, to add
information concerning the distribution of fluorophores with
respect to depth beneath the tissue surface.
Determining a Model Relating Fluorescence Properties to Disease
State or Chemical Changes
[0063] The relationship between tissue fluorescence properties at
one or more wavelengths and diabetes disease state is typically not
apparent upon visual inspection of the spectral data. Because this
is the case, it is usually necessary that a multivariate
mathematical relationship, or `model`, be constructed to classify
tissue disease states or to quantify chemical changes using
intrinsic fluorescence spectra. The construction of such a model
generally occurs in two phases: (i) collection of `calibration` or
`training` data, and (ii) establishing a mathematical relationship
between the training data and the disease states or reference
concentrations represented in the training data.
[0064] During the collection of training data, it can be desirable
to collect fluorescence data from many individuals, representing
all disease states or reference values one wishes to characterize
with the model to be constructed. For example, if one wishes to
construct a model that separates diabetics from nondiabetics, it
can be desirable to collect representative spectra from a wide
variety of both types of individuals. It can be important to
collect these data in a manner that minimizes the correlation
between disease state and other parameters that can result in
fluorescence variation. For example, the natural formation of
collagen AGEs in health results in a correlation between skin AGE
content and chronological age. It can be important, therefore, to
obtain spectra from diabetics and nondiabetics spanning the ages
for which the classification model is desired to be applicable.
Alternatively, if one wished to construct a model that quantified
the level of a specific skin collagen AGE, it can be advisable to
collect spectroscopic data spanning a wide range of AGE reference
values each day rather than to measure all individuals having the
smallest AGE concentrations early in the study and all individuals
with larger AGE concentrations later in the study. In the latter
case, a spurious correlation arises between AGE concentration and
time, and if there are instrumental trends over the course of the
study, the resulting model might be calibrated to instrument state
rather than analyte concentration.
[0065] As the training data are collected, additional reference
information can be collected in order to later construct an
appropriate classification model. For example, if the
classification model is to predict diabetic state, the diabetes
status of some or all of the individuals represented in the
training set can be collected and associated with the corresponding
spectroscopic training data. Alternatively, the classification
model can predict the level of a certain chemical species in the
skin, such as glycated collagen, glycated elastin, a specific AGE
such as pentosidine or CML, or other proteins modified by the
hyperglycemic conditions associated with diabetes mellitus. In
these cases, skin biopsy specimens can be collected from
individuals during the collection of training data. In addition, if
other ancillary information, such as age, body mass index, blood
pressure, HbA1c, etc. is to be used in generating later disease
state assessments, this information can be collected for some or
all spectra in the training set.
[0066] After the training data are collected, a multivariate model
can be constructed to relate the disease states associated with the
training data to the corresponding spectroscopic information. The
exact model can be chosen based upon the ultimate goal of the
training phase. There are at least two types of multivariate models
that one might construct. In the first, the goal of the training
process is to create a model that correctly classifies the disease
state of the measured tissue. In this case, the output of the model
is an assignment to one or more discrete classes or groups. These
classes or groups might represent different grades or
manifestations of a particular disease. They might also represent
various degrees of risk for contracting a particular disease or
other subgroups of the population that are pertinent to the disease
state in question. For the second model type, the goal is to
provide a quantitative estimate of some diabetes-induced chemical
change in the system. The output of this model is continuously
variable across the relevant range of variation and is not
necessarily indicative of disease status.
Classification of Tissue Disease Status
[0067] The model-building steps typically followed when the end
goal is to use the model to assess tissue disease state are
depicted diagrammatically in FIG. 4. The first step, spectral
preprocessing, involves pre-treatment, if any, of the spectral data
including, for example, background-correction and
intrinsic-fluorescence correction steps as described above. In the
second step, the dimensionality of the data set can be reduced by
employing a factor analysis method. Factor analysis methods allow
an individual spectrum to be described by its scores on a set of
factors rather than the spectral intensities at each collected
wavelength. A variety of techniques can be utilized in this step;
Principal Components Analysis (PCA) is one suitable method. The
factors generated, for example, by Partial Least-Squares (PLS)
regression onto a reference variable associated with disease status
can also be used. After the factors have been generated, those
factors that are most useful for classification can be selected.
Valuable factors typically exhibit a large separation between the
classes while having low within-class variance. Factors can be
chosen according to a separability index; one possible method for
calculating the separability index for factor f is:
f corr ( .lamda. x , .lamda. m ) = F meas ( .lamda. x , .lamda. m )
R meas ( .lamda. x ) k R meas ( .lamda. x ) n ; n , k < 1 Eq 3
##EQU00004##
[0068] where x.sub.1,f is the mean score for class 1, x.sub.2,f is
the mean score for class 2, and s.sup.2 represents variance of the
scores within a class.
[0069] Finally, a technique for separating the data into the
various classes can be selected. A variety of algorithms can be
suitable, and the optimum algorithm can be selected according to
the structure of the training data. In Linear Discriminant Analysis
(LDA), a single linear function that best separates the
multidimensional spectroscopic data into the reference classes
observed in the training period is constructed. In Quadratic
Discriminants Analysis, a quadratic discriminant function is
constructed. FIG. 5 illustrates the manner in which the
discriminant function might find the best separation between two
groups--it depends on the structure of the data. In some cases
(FIG. 5 (a)), a linear discriminant function is sufficient to
separate the classes. As the multi-dimensional structure of the
classes becomes more complex, however, more sophisticated
classifiers, such as quadratic functions, are required (FIG. 5(b)).
In some situations (FIG. 5(c)), the structure of the data makes
even quadratic discriminant analysis difficult and other
classification methods are more appropriate.
[0070] A number of suitable classification algorithms exist. For
example, k-nearest neighbors, logistic regression, hierarchical
clustering algorithms such as Classification and Regression Trees
(CART), and machine learning techniques such as neural networks,
can all be appropriate and useful techniques. A detailed discussion
of such techniques is available in Huberty, Applied Discriminant
Analysis, Wiley & Sons, 1994 and Duda, Hart, and Stork, Pattern
Classification, Wiley & Sons, 2001.
Quantitation of Diabetes-Induced Chemical Modifications
[0071] If the end goal is to quantify the concentration of an
analyte or a class of analytes that are embedded in the tissue, a
different approach can be taken in the model-building process. In
this case, a set of (typically continuous) reference values for the
analyte(s) in question can be obtained for some or all spectra in
the training set. For example, in the event that the model is to
quantify the level of pentosidine in skin collagen, the reference
concentrations associated with each spectrum in the training set
can come from pentosidine assays conducted on skin punch biopsy
specimens obtained during calibration. In the event that the biopsy
process is too invasive for the study participants, some surrogate
for AGE-related chemical changes can also be used. For example,
under the assumption that FPG values increase as the degree of
diabetes progression increases, a reasonable compromise can collect
FPG data as a surrogate for skin AGE concentration. HbA1c and OGTT
information can be used similarly.
[0072] Calibration models used to predict quantitative values
associated with a test set can be constructed by forming a
mathematical relation between reference values and associated
spectral data. A variety of algorithms are suitable. For example,
in Principal Components Regression (PCR) the calibration data are
first decomposed into a set of orthogonal scores and loadings, and
then the reference values are regressed onto the scores of the
first N PCA factors. Another suitable method is Partial
Least-Squares (PLS) regression, in which a set of factors are
constructed so that the squared covariance between the reference
values and the scores on each successive PLS loading vector is
maximized. These procedures and others have been summarized by
Martens and Naes in Multivariate Calibration, Wiley & Sons
(1989).
[0073] Quantitative calibration models are certainly not limited to
the regression techniques described here. Those skilled in the art
will recognize that a variety of other approaches is available,
including other regression techniques, neural networks, and other
nonlinear techniques.
Determining Disease State or Chemical Changes from a Fluorescence
Property
[0074] After model construction, fluorescence measurements can be
made on new specimens having an unknown disease state or
diabetes-related chemical change. The method by which the disease
state or chemical properties of the new specimen are determined can
be dependent of the type of model constructed in the training
phase.
Classification of Tissue Disease Status
[0075] As mentioned above, a variety of models is available for
discrimination of various diabetic states from measured
fluorescence properties. For example, when the method of Quadratic
Discriminants Analysis is used, the new fluorescence spectrum is
projected onto the factors created with the training data during
construction of the classification model, creating a new vector of
scores, x.sub.i, for the test spectrum. The means x.sub.j and
covariance matrices S.sub.j of the scores of the training set over
the previously-selected factors are computed for each class j. For
example, j=1,2 for a two-class (i.e., diabetic vs. non-diabetic)
problem. The Mahalanobis distance, D.sub.i,j, from sample i to
class j, then is computed for each vector of scores (x.sub.i)
by
D.sub.i,j=(x.sub.i- x.sub.j).sup.TS.sub.j.sup.-1(x.sub.i- x.sub.j).
Eq 11
[0076] The posterior probability that test sample i is a member of
class j, p(i.epsilon.j), can be calculated using Equation 12. As
with all probabilities, this number ranges between 0 and 1;
probabilities close to 1 indicate that an observation lies close to
the diabetic class, and probabilities close to 0 indicate that an
observation lies close to the non-diabetic class. The probability
that sample i is a member of class j is given by
p ( i .di-elect cons. j ) = .pi. ij - D ij / 2 j .pi. ij - D ij / 2
, Eq 12 ##EQU00005##
where .pi..sub.ij are the prior probabilities that test sample i is
a member of class j based on other knowledge (risk factors, etc.).
The prior probabilities are parameters that can be tuned in the
prediction phase depending, in part, on the diagnostic application
of the classification algorithm.
[0077] Finally, a threshold can be applied that assigns the new
fluorescence measurement to a particular tissue disease state. For
example, it might be determined that all fluorescence measurements
yielding a posterior probability of diabetes greater than 0.75 will
be assigned to the diabetic class. Like the prior probabilities,
the exact threshold applied in validation can depend on a variety
of factors, including the application, disease prevalence, and
socioeconomic ramifications of positive and negative test
results.
Quantitation of Diabetes-Induced Chemical Modifications
[0078] The output of a quantitative calibration model can be a
regression vector that converts the corrected fluorescence spectrum
into a quantitative analyte prediction via an inner product:
a=F.sub.corr.cndot.b, Eq 13
where a is the analyte prediction and b is the regression
vector.
[0079] The method for generating a quantitative output can vary
with the model constructed in the training phase. Final analyte
quantitation with, for example, a neural network proceeds by a
different process but yields a similar output.
[0080] After the construction of either type (i.e., a quantitative
model for chemical change or a classification model for tissue
disease state) of multivariate model, the accuracy of the model can
be tested by predicting the disease status associated with
well-characterized `validation` spectra. A variety of techniques
also exist for accomplishing this task. In leave-one-out
cross-validation, a single spectrum or set of spectra from the
training set are omitted from the model-building process, and then
the resulting model is used to predict the disease status
associated with the spectra left out of the model. By repeating
this process a sufficient number of times, it is possible to
develop a mathematical estimate of the performance of the model
under new conditions. A more rigorous test of the newly-constructed
model is to apply the model to an entirely new data set, or a
`test` set. In this case, the disease status associated with each
spectrum is known, but the `test` spectra are collected at a
different time (e.g., subsequent to model-building) than the
training data. By comparing the predictions on the `test` data to
the reference values associated with these data, the diagnostic
accuracy of the model in question can be assessed independent of
the training data.
EXAMPLE EMBODIMENTS
[0081] FIGS. 6-10 depict the results of a large calibration study
conducted over a period of 3 months. In these experiments, a
commercially-available fluorimeter (SkinSkan, Jobin-Yvon, Edison,
N.J., USA) was used to acquire noninvasive fluorescence and
reflectance spectra from the skin of the volar forearm in study
participants. In the training phase, 57 Type 2 diabetic and 148
nondiabetic subjects were measured by fluorescence spectroscopy.
Study participants were selected on the basis of their age and
self-reported diabetes status. In addition to the subjects' own
report of their disease status, FPG and OGTT reference information
were also collected for all diabetics and a fraction of the
nondiabetics in the study. For these individuals, FPG and 2-hour
OGTT values were collected on each of two different days.
Spectroscopic measurements were collected on a third day, and no
specific fasting requirements or other pre-test preparations were
imposed on the study participants.
[0082] In this study, several fluorescence data sets were acquired.
Three different sets of emission scans were collected at 2.5-nm
data spacing: (1) x=325 nm, m=340-500 nm, (2) x=370 nm, m=385-500
nm, and (3) x=460 nm, m=475-550 nm. In addition, three different
sets of excitation scans (2.5 nm data spacing) were also collected:
(1) m=460 nm, x=325-445 nm, (2) m=520 nm, x=325-500 nm, and (3)
m=345 nm, x=315-330 nm. A lower-resolution (10-nm data spacing)
excitation-emission map (EEM) was also collected, along with skin
reflectance data spanning the range of excitation and emission
wavelengths used in the fluorescence data acquisition. These data
sets and their corresponding wavelength regions are depicted
graphically in FIG. 6, in which the black open circles denote
excitation scans, the gray filled circles denote emission scans,
the gray x symbols denote the EEM, and the black x symbols denote
reflectance scans. Two replicates of each of these data sets were
acquired for each study participant. Each replicate spectroscopic
dataset was obtained from a different physical region of the volar
forearm.
[0083] Two different multivariate models were constructed with
these training data. The first model classifies new measurements
according to their apparent diabetic status. The second model
quantifies diabetes-induced chemical changes using the FPG
reference values as a surrogate for skin-collagen AGE content.
Classification of Tissue Disease Status
[0084] After the completion of the training data collection, all of
the noninvasive measurements were pooled along with the reference
information (self-reported diabetes status, FPG and OGTT reference
values). Post-processing, including intrinsic fluorescence
correction using the method described in Eq. 3 with k=0.5 and
n=0.7, was first performed on all fluorescence data. The results
presented here were obtained by combining the three excitation
scans described above into a single large fluorescence spectrum.
The PCA factor analysis method was used to reduce the
dimensionality of this data set, and QDA was used to construct a
classifier using the scores on 5 of the first 25 principal
components using the separability index indicated in Equation 6 to
identify those PCA factors most useful for class discrimination.
The diagnostic accuracy of the QDA classifier was assessed using
the method of leave-one-out cross-validation. In this instance, all
of the spectroscopic data for a single patient is held out from the
training data, an independent QDA model is constructed, and the
posterior probability of each spectrum's membership in the diabetic
class is computed. FIG. 7 is a box-and-whisker plot of
cross-validate posterior probabilities of membership in the
diabetic class for all study participants. It can be seen that the
known diabetic individuals, in general, exhibit higher
probabilities for diabetes than the nondiabetics. As is often the
case with diagnostic tests, no single test threshold perfectly
separates all diabetics from all nondiabetics with the example
data.
[0085] One way of summarizing the diagnostic accuracy of the QDA
classifier is to plot the True Positive Fraction (i.e., the
sensitivity) vs. False Positive Fraction (i.e., 1-specificity) for
a range of test thresholds. The area under the resulting
Receiver-Operator Characteristic (ROC) curve approaches unity for a
perfect classification test and approaches 0.5 for tests that are
no better than random chance. The ROC curve from the QDA
cross-validation procedure described above is shown as the solid
line in FIG. 8. The area under this ROC curve is 0.82, and at the
knee of the curve, a sensitivity of approximately 70% is achieved
when the false positive rate is approximately 20%. The associated
equal error rate, the point at which the sensitivity and false
positive rate are equal, is approximately 25%. All of these ROC
parameters compare favorably with comparable values from the FPG
ROC curve, which is shown as a dashed line for comparison. The ROC
curve for the FPG test was computed from a database of over 16,000
individuals participating in the Third National Health and
Nutrition Examination Survey, conducted from 1988-1994. The curve
was generated by applying various test thresholds to the FPG test
values using the study participants' self-declared diabetic status
as truth.
Quantitation of Diabetes-Induced Chemical Modifications
[0086] Rather than using fluorescence measurements to directly
assign a diabetes disease status to an unknown specimen, it can be
valuable to generate a quantitative measure of chemical changes
that is related to the presence or progression of diabetes. For
example, skin biopsies can be assayed for the concentration of
pentosidine, CML, or another skin collagen AGE. Those reference
values can be used in the construction of a multivariate model as
described above. In the current example, such reference data were
not available, and the FPG values collected during the training
phase were used as surrogates for this chemical information.
[0087] A quantitative PLS calibration model was constructed from
the same corrected fluorescence data described above. The results
presented here were obtained by combining the three excitation
scans described above into a single large fluorescence spectrum. A
total of three latent variables, or PLS factors, were constructed
from the noninvasive fluorescence data and used to model the
variation in the FPG reference values. Because most of the
fluorescence wavelengths are centered around the CLF window, the
spectroscopic changes are presumed to originate, at least in part,
with collagen crosslinking and associated diabetes progression. As
a result, it is not expected that the FPG test values will serve as
perfect surrogates for disease progression.
[0088] Results of a cross-validation in which all data from a
single study participant were rotated out in each iteration are
presented in FIG. 9. The PLS estimates at three model factors are
depicted on the y-axis; because the fluorescence changes are
presumed to originate with AGE chemistry, this axis is labeled
`Chemical Progression`, and the dimensions are left arbitrary. The
corresponding FPG value is indicated on the abscissa. Values from
diabetic subjects are depicted as solid gray circles, while
non-diabetics are represented by open circles. It can be seen that,
in general, larger reference values correspond to larger PLS
estimates of Chemical Progression, although, as one might expect,
the relationship is not perfectly linear. In addition, it can be
seen that diabetic individuals exhibit, on average, larger Chemical
Progression estimates than do nondiabetic individuals. A reference
value more closely aligned with true disease progression, such as
one more or skin-collagen AGEs, could produce a model with a more
linear relationship.
[0089] Although a quantitative model for diabetes-related chemical
changes might report only a test value (i.e., without rendering a
classification regarding the tissue's disease status), it is also
possible to use the output of such a model for classification
purposes. One example of such a procedure is illustrated in FIG.
10, which is a ROC curve created from the PLS Chemical Progression
estimates depicted in FIG. 9 using the study participants'
self-reported diabetic status as truth. The FPG ROC curve from FIG.
8 is reproduced in FIG. 10 for comparison. The area under this ROC
curve is 0.81, and at the knee of the curve, a sensitivity of 65%
is achieved at a 20% false positive rate. The associated equal
error rate, the point at which the sensitivity and false positive
rate are equal, is approximately 25%. All of these ROC parameters
again compare favorably with comparable values from the FPG ROC
curve.
Example Apparatus
[0090] Components or sub-systems of an apparatus to characterize
and/or quantify disease state by tissue fluorescence are
illustrated in FIG. 11. An illumination subsystem comprises a light
source A suitable to illuminate the tissue and thereby
electronically excite endogenous chromophores within the tissue.
Illumination subsystem includes an optical system B that couples
the light produced by the light source A to the tissue and collects
the resulting fluorescent light from the tissue sample and couples
the collected fluorescence to a detection sub-system C. In the
detection subsystem, the fluorescent light is typically converted
into an electrical signal. The signal corresponding to the tissue
fluorescence is measured and characterized by an analysis or data
processing and control system D. The processing/control system can
also control or modify the actions of the other sub-systems.
[0091] Example I of such a system embodies a high-intensity arc
lamp, shutter, monochromator and collimator as the core elements of
the light source. The optical-coupling sub-system is comprised of a
bifurcated fiber bundle that couples the excitation light to the
tissue and collects fluorescence emanating from the tissue. The
second leg of the bifurcated bundle couples the collected
fluorescent light to the detection sub-system. The detection system
contains a monochromator (separate from the monochromator of
component A) and a detector such as a photomultiplier. The
electrical signal corresponding to the tissue fluorescence is
digitized, processed and stored by a computer (Component D). The
computer also controls functions of other sub-systems such as the
tuning of monochromators and opening closing shutters.
[0092] In Example II, the bifurcated fiber-optic bundle of Example
I is replaced by a system of lenses and mirrors to convey
excitation light from the light source to the tissue and then
collect emitted fluorescence from the tissue and relay it to the
detection sub-system.
[0093] In Example III, the broadband light source of Example I
consisting of the high-intensity arc lamp and monochromators is
replaced by one or more discrete sources such as LEDs or laser
diodes. The LEDs can require suitable optical bandpass filters to
produce excitation light that is sufficiently narrow in wavelength.
The LEDs or laser diodes can be operated in a continuous wave,
modulated or pulsed manner. The output of these sources is coupled
to the tissue by an optical sub-system such as the fiber optic
bundle of Example I or a collection of mirrors and/or lenses as
described for Example II.
[0094] In Example IV, the detection system of Example I comprised
of a monochromators and single detector is replaced by a
spectrograph and a detector array or CCD array.
[0095] An example of a skin fluorimeter is presented in FIG. 12.
The illumination sub-system consists of a xenon arc lamp coupled to
a double monochromator. The spectrally narrow output from the
monochromator is coupled into a bifurcated fiber bundle. The fibers
in the ferrule contacting the tissue can be arranged randomly, as
shown in FIG. 13, or designed with specific source-detector fiber
spacing, as illustrated in FIG. 14, can be constructed. An example
of a fixture--in this instance, a forearm cradle--to hold the fiber
bundle in contact with the skin of the subject is shown in FIG. 15.
The cradle provides a means for the subject to comfortably rest
their arm while the underside forearm skin is in contact with the
delivery/collection end of the fiber bundle. The cradle also
facilitates reproducible positioning of the volar forearm site with
respect fiber optic bundle. The fluorescence collected by the
detector fibers within the bifurcated bundle form the entry slit to
a second monochromator of the fluorimeter depicted in FIG. 12. The
monochromator filters the incoming fluorescent light and allows a
narrow band to fall on the detector, a photomultiplier tube (PMT)
or a channel photomultiplier tube. The PMT could be replaced by a
sufficiently sensitive silicon avalanche photodiode or regular
silicon photodiode. Tunable grating pairs in both the source and
detector monochromators allow for the wavelength of each section to
be independently tuned. The signal from the PMT is digitized and
recorded by a computer that also tunes the gratings, adjusts
detector and controls the monochromator shutters.
[0096] It can be useful to preferentially collect information from
the dermis. FIG. 14 is an illustration of a tissue interface
suitable for use in the present invention. The tissue interface
comprises a plurality of excitation fibers, in optical
communication with a light source and adapted to deliver excitation
light to the tissue. It further comprises a plurality of receive
fibers, in optical communication with a detector and adapted to
receive light emitted from the tissue in response to the excitation
light. The receive fibers are spaced apart, and disposed relative
to the excitation fibers such that fluorescence information is
preferentially collected from the dermis layer of the skin without
requiring physical exposure of the dermis.
[0097] As discussed previously, it can also be useful to
preferentially collect information from the dermis via multiple
channels to allow for measurement of optical properties of tissue.
FIG. 16 is an illustration of a tissue interface suitable for use
in the present invention. The tissue interface comprises a
plurality of excitation fibers (shown, for example, as solid
circles) in optical communication with a light source and adapted
to deliver excitation light to the tissue. It further comprises a
plurality of receive fibers (shown, for example, as both open and
horizontal line hatched circles) in optical communication with a
detector and adapted to receive light emitted from the tissue in
response to the excitation light. In the illustration, the open
circles comprise a first channel of receive fibers and the hatched
circles comprise a second channel of receive fibers. In each of the
channels the receive fibers are spaced apart, and disposed relative
to the excitation fibers such that fluorescence information is
preferentially collected from the dermis layer of the skin without
requiring physical exposure of the dermis. Light collected from the
skin by each of the receive channels is individually detected
either by multiple detectors or through switching between the
channels to a single detector.
[0098] FIGS. 17 and 18 depict other arrangements of excitation and
receive fibers to allow for multiple channels of information to be
collected. FIG. 17 shows a circular arrangement of fibers wherein
the central (solid circle) fiber delivering excitation light is
surrounded by a first channel (open circles) of receive fibers,
which is further surrounded by a second channel (hatched circles)
of receiver fibers. FIG. 18 shows a linear arrangement of fibers
wherein a plurality of excitation fibers (solid circles) are
aligned in a row. A first channel of receive fibers (open circles)
are positioned in a row parallel to, and some distance from, the
excitation row. A second channel of receive fibers (hatched
circles) is also positioned in a row parallel to, and some further
distance from, the excitation row.
[0099] FIGS. 19-22 show various views of possible arrangements of a
multiple-channel fiber optic tissue probe relative to the sampling
surface. FIG. 19 is a schematic depiction of a sectional view of
part of a multiple-channel fiber optic tissue probe of a vertical
arrangement, wherein the solid fiber can represent an excitation
fiber, the open fiber a first receive channel, and the line hatched
fiber a second receive channel. In this arrangement the separation
between the excitation fiber and first and second receive channels
can be chosen so as to proved desired information useful in the
determination of tissue optical properties. FIG. 20 is a schematic
depiction of a sectional view of part of a multiple-channel fiber
optic tissue probe of a tilted arrangement. The tilt angle,
.alpha., from normal of the excitation fiber may be from 0 to 60
degrees. Likewise, the tilt of the first and second receive
channels (open and hatched fibers, respectively) may be tilted in
the opposite direction of the excitation fiber from 0 to 60
degrees, and do not necessarily need to be tilted at an equal and
opposite amount. FIG. 21 is a schematic depiction of a sectional
view of part of a multiple-channel fiber optic tissue probe of a
tilted arrangement. Here the first and second receive channels are
placed on either side of a central excitation fiber. FIG. 22 is an
isometric view showing how several tiled fibers can be arranged in
order to increase the light throughput.
[0100] FIG. 23 is an illustration of a multiple-channel fiber optic
tissue probe interrogating a tissue volume at various excitation
and receiver separations. In each of the four illustrations there
is a single tilted excitation fiber denoted by an arrow point
downward toward a tissue volume shown in black. Opposed to the
excitation fiber are four receive fiber channels, each separated a
distance away from the excitation fiber. From left to right, the
illustrations show the region of tissue interrogated as a function
of excitation fiber and receive channel separation. These separate
receive channels allow for the preferential collection of
information from the dermis which can be useful for the measurement
of optical properties of tissue.
[0101] Those skilled in the art will recognize that the present
invention can be manifested in a variety of forms other than the
specific embodiments described and contemplated herein.
Accordingly, departures in form and detail can be made without
departing from the scope and spirit of the present invention as
described in the appended claims.
* * * * *