U.S. patent application number 12/568363 was filed with the patent office on 2010-05-20 for systems and methods for correcting optical reflectance measurements.
This patent application is currently assigned to University of Massachusetts, a Massachusetts corporation. Invention is credited to Michael A. Shear, Babs R. Soller, Olusola O. Soyemi, Ye Yang.
Application Number | 20100123897 12/568363 |
Document ID | / |
Family ID | 37215486 |
Filed Date | 2010-05-20 |
United States Patent
Application |
20100123897 |
Kind Code |
A1 |
Yang; Ye ; et al. |
May 20, 2010 |
SYSTEMS AND METHODS FOR CORRECTING OPTICAL REFLECTANCE
MEASUREMENTS
Abstract
We disclose measurement systems and methods for measuring
analytes in target regions of samples that also include features
overlying the target regions. The systems include: (a) a light
source; (b) a detection system; (c) a set of at least first,
second, and third light ports which transmit light from the light
source to a sample and receive and direct light reflected from the
sample to the detection system, generating a first set of data
including information corresponding to both an internal target
within the sample and features overlying the internal target, and a
second set of data including information corresponding to features
overlying the internal target; and (d) a processor configured to
remove information characteristic of the overlying features from
the first set of data using the first and second sets of data to
produce corrected information representing the internal target.
Inventors: |
Yang; Ye; (Worcester,
MA) ; Soller; Babs R.; (Northboro, MA) ;
Soyemi; Olusola O.; (Shrewsbury, MA) ; Shear; Michael
A.; (Northbridge, MA) |
Correspondence
Address: |
FISH & RICHARDSON PC
P.O. BOX 1022
MINNEAPOLIS
MN
55440-1022
US
|
Assignee: |
University of Massachusetts, a
Massachusetts corporation
|
Family ID: |
37215486 |
Appl. No.: |
12/568363 |
Filed: |
September 28, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11411538 |
Apr 25, 2006 |
7616303 |
|
|
12568363 |
|
|
|
|
60674379 |
Apr 25, 2005 |
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Current U.S.
Class: |
356/300 ;
356/445 |
Current CPC
Class: |
G01N 21/359 20130101;
A61B 5/14551 20130101; G01N 21/59 20130101; A61B 5/0075 20130101;
A61B 5/6824 20130101; A61B 5/14539 20130101; A61B 5/14546 20130101;
A61B 5/14535 20130101; G01N 21/25 20130101; G01N 21/49 20130101;
G01N 21/274 20130101; G01N 2021/3155 20130101; G01N 21/55 20130101;
A61B 5/1455 20130101 |
Class at
Publication: |
356/300 ;
356/445 |
International
Class: |
G01J 3/00 20060101
G01J003/00; G01N 21/55 20060101 G01N021/55 |
Goverment Interests
STATEMENT AS TO FEDERALLY SPONSORED RESEARCH
[0002] Funding for the work described herein was provided by the
National Space Biomedical Research Institute, Grant Number
SMS00403, funded under NASA Cooperative Agreement NCC 9-58 and the
U.S. Army Congressionally Directed Medical Research Program, Grant
Number DAMD17-03-1-0005. The Federal Government may have certain
rights in the invention.
Claims
1. A measurement system, comprising: (a) a light source; (b) a
detection system; (c) a set of at least first, second, and third
light ports, which set transmits light from the light source to a
sample and receives and directs light reflected from the sample to
the detection system, wherein a distance between the first port and
the third port comprises a first detection distance and a distance
between the second port and the third port comprises a second
detection distance; wherein the first detection distance is larger
than the second detection distance; and wherein either (i) the
first and second ports are transmitting ports and the third port is
a receiving port, or (ii) the first and second ports are receiving
ports and the third port is a transmitting port; and wherein the
detection system generates a first set of data corresponding to the
first detection distance and comprising information corresponding
to both an internal target within the sample and features overlying
the internal target, and a second set of data corresponding to the
second detection distance and comprising information corresponding
to features overlying the internal target; and (d) a processor
configured to remove information characteristic of the overlying
features from the first set of data using the first and second sets
of data to produce corrected information representing the internal
target.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, U.S. patent application Ser. No. 11/411,538 entitled "SYSTEMS
AND METHODS FOR CORRECTING OPTICAL REFLECTANCE MEASUREMENTS," filed
on Apr. 25, 2006, which claims priority to U.S. Provisional
Application Ser. No. 60/674,379 entitled "SYSTEMS AND METHODS FOR
CORRECTING OPTICAL REFLECTANCE MEASUREMENTS," filed on Apr. 25,
2005. The entire contents of each of the foregoing applications are
incorporated herein by reference.
TECHNICAL FIELD
[0003] This invention relates to spectrometer systems and methods,
and more particularly to spectrometer systems for reflectance
measurements.
BACKGROUND
[0004] Optical spectroscopy can be used to determine the
concentration of chemical species in gaseous, liquid, and solid
samples. The amount of light absorbed by a particular chemical
species is often linearly related to its concentration through
Beer's Law, A=.epsilon.lc, where A is the absorbance of the
chemical species, .epsilon. is a constant specific to the chemical,
l is the path length of light, and c is the concentration of the
chemical. When incident light with an intensity, I.sub.0, is
incident on the sample, and I is the intensity of light after it
has passed through a solution containing the chemical to be
measured, the absorbance is given by A=log (I.sub.0/I).
[0005] For nontransparent materials, including complex materials
such as powders, tablets, natural materials (e.g., soil,
agricultural products), blood, skin, and muscle, optical
information can be collected via diffuse reflectance spectroscopy.
In this setting, A=(I.sub.100/I.sub.R), where I.sub.100 is the
light reflected from a 100% diffuse reflectance standard, the
equivalent of the incident light, and I.sub.R is the light
reflected from the sample under study. The concentration of a
chemical component in one of these complex materials is related to
A, though often not linearly. More sophisticated mathematical
techniques, such as, for example, partial least squares regression
and other multivariate calibration methods are used to determine a
relationship between concentration and absorbance. Once these
calibration models are derived, they can be used to determine
chemical composition by measuring absorbance in transmittance or
reflectance mode.
[0006] Diffuse reflectance spectroscopy techniques with near
infrared spectroscopy ("NIRS") have been used for the noninvasive
measurement of blood and tissue chemistry in human and animal
subjects. NIRS (e.g., using a wavelength range of about 650-1000
nm) can be used to measure a number of important medical parameters
such as tissue oxygenation, tissue pH, blood hematocrit ("Hct"),
and glucose, but its widespread application in medicine has been
hindered by both the inter- and intra-subject analyte-irrelevant
variation in tissue overlying as well as inside the structures to
be measured. For example, when diffuse reflectance NIRS is used to
measure blood hematocrit in muscle or organs, the accuracy of the
measurements can be affected by absorbance variations in layers
overlying the muscle or organs (e.g., due to variations in the
thickness of fat and skin layers between different patients in a
patient population or between different locations on an individual
patient) and/or spectral interference from structural variations in
muscle and/or organs that are irrelevant to the measurements.
[0007] Near infrared light can penetrate through a subject's skin
and bone to provide information on chemical species present in
blood and underlying tissue. For example, pulse oximetry, a
ubiquitous hospital monitoring system that measures arterial
hemoglobin oxygen saturation, is based on two-wavelength NIRS.
Multi-wavelength NIRS, in combination with chemometrics (i.e.,
statistics-based methods of analyzing complex spectra), can provide
a platform technology for the noninvasive measurement of several
additional analytes present in the blood and tissue. NIRS can
provide accurate and continuous measurement of medical analytes
without the need to remove a blood or tissue sample from the
patient. The application of this technique involves shining near
infrared light onto the skin directly or through a fiber optic
bundle and measuring the spectrum of the light that is reflected
back from the blood containing muscle. While near-infrared
absorption by hemoglobin and deoxyhemoglobin has been used to
measure oxygen saturation levels in various tissue beds,
multi-wavelength spectroscopy in combination with additional
mathematical techniques often is required to measure additional
important blood and tissue analytes. Chemometrics is a branch of
chemistry that provides statistics-based techniques to process
multi-wavelength spectra such that analyte concentration can be
calculated from the reflectance spectra recorded from complex media
such as biological tissue.
[0008] Chemometrics is used to derive a mathematical relationship
between relevant portions of the spectra collected from a sample
and the concentration or amount of the analyte of interest in the
sample. The relationship between the spectra and the chemical
concentration can be expressed as a "calibration equation" that can
be programmed into a patient monitor and used to determine analyte
concentrations based on the measured reflectance spectra. Spectra
collected from patients can be processed through calibration
equation(s) stored in the patient monitor, and the analyte
concentration in those patients can be reported based on the
collected spectra and the calibration equations. Because the
optical reflectance technique is noninvasive, the medical
measurement can be updated as often as spectra are collected,
usually on the order of a few seconds. The feasibility of using
this method has been demonstrated on the bench, in animals, and in
human subjects for the assessment of blood hematocrit, glucose,
cholesterol, electrolytes, lactate, myoglobin saturation, muscle
pH, and oxygen tension ("PO.sub.2").
[0009] When calibration equations are developed using chemometrics,
at least two sets of data are collected. A set of NIRS spectra is
recorded approximately simultaneously with an independent, reliable
measurement of the analyte over the entire physiologic and
pathophysiologic range. For example, if one wanted to develop a
calibration equation to determine blood hematocrit from measured
reflectance spectra, several spectra from subjects would be
compared with blood samples taken from those subjects and analyzed
for hematocrit in a clinical laboratory. A chemometric technique,
such as, for example, partial least squares ("PLS") regression can
be used to identify and correlate portions of the spectrum to the
measured hematocrit. The regression coefficients are used to
generate the calibration equation.
[0010] Then, when subsequent reflectance spectra are collected from
other patients, the regression coefficients can be combined with
the spectra of the other patients to produce the NIRS-determined
hematocrit value for the other patients. An advantage of using a
chemometric technique such as PLS to derive a calibration equation,
rather than simple linear regression, is that PLS is adept at
establishing correlation between spectra and analytes when the
analyte spectra are complicated by other absorbing species and
scattering elements (like cells and muscle fibers).
[0011] For the calibration equation to perform accurately on
patients over a wide range of instrumental and environmental
conditions and different patient characteristics, the data in the
calibration data set should cover as wide a range of values as
possible and should encompass the entire clinically significant
range. It is also important that the data be collected under the
type of variable patient conditions that might affect the NIRS
spectra. Conditions that affect spectra include variation in
temperature, water content, and the presence of interfering
chemical agents used to treat the patient. This helps ensure that
the calibration equations are accurate when used on future
subjects, because the effect of the interfering agents is modeled
as part of the calibration equation.
[0012] Widespread application of NIRS for medical measurement has
been hampered by both inter- and intra-subject variation in tissue
overlying the target tissue, such as muscle, or the target organ.
Additionally, NIRS measurement techniques have been limited by
their inaccurate performance due to short-term changes in skin
blood flow or due to long-term variation in skin surface and
texture during wound healing.
SUMMARY
[0013] The invention is based, at least in part, on the discovery
that when a spectrometer is used to record reflectance spectra from
a sample, a short distance between the light source used to
illuminate the sample and the detector used to measure reflected
light results in recording of spectra that are sensitive to
features that are relatively close to the surface of the sample,
while a longer source-detector distance results in spectra that are
sensitive to both surface features and deeper lying (e.g.,
internal) features of the sample. Correcting spectra recorded with
a long source-detector spacing against spectra recorded with a
short source-detector spacing can remove the spectral features of
the overlying features from the spectra of the internal, deeper
lying features. The spectra can be further corrected to remove
features that arise from variations in optical scattering
properties of the internal features that are not related to
measurements of an analyte of interest in the underlying
layers.
[0014] Spectra can be recorded using light of different
wavelengths. For example, the light source can provide light in one
or more of the near-infrared, infrared, visible, ultraviolet, and
other regions of the electromagnetic spectrum.
[0015] In a first aspect, the invention features measurement
systems that include: (a) a light source; (b) a detection system;
(c) a set of at least first, second, and third light ports which
transmits light from the light source to a sample and receives and
directs light reflected from the sample to the detection system,
where a distance between the first port and the third port
corresponds to a first detection distance, a distance between the
second port and the third port corresponds to a second detection
distance, and the first detection distance is larger than the
second detection distance; and (d) a processor. Either (i) the
first and second ports are transmitting ports and the third port is
a receiving port, or (ii) the first and second ports are receiving
ports and the third port is a transmitting port. The detection
systems generate a first set of data corresponding to the first
detection distance and including information corresponding to both
an internal target within the sample and features overlying the
internal target, and a second set of data corresponding to the
second detection distance and including information corresponding
to features overlying the internal target. The processor is
configured to remove information characteristic of the overlying
features from the first set of data using the first and second sets
of data to produce corrected information representing the internal
target.
[0016] Embodiments can include any of the following features.
[0017] The set of at least first, second, and third ports can be
situated on a single probe. The second detection distance can be
between about 1 mm and about 5 mm, for example, such as between
about 1.5 mm and about 3.5 mm. The first detection distance can be
greater than about 10 mm (e.g., greater than about 15 mm, greater
than about 20 mm, greater than about 30 mm, greater than about 50
mm). The system can include a shutter system for controlling
whether light from the first or second transmitting port
illuminates the sample.
[0018] The probe head can include a thermally conductive material
to dissipate heat from the sample. The system can further include a
thermally-conductive bridge between light transmitting ports and
light receiving ports.
[0019] The light source can provide light in the near-infrared
region of the electromagnetic spectrum. The light source can
include at least one of an incandescent light source element, a
light emitting diode, a laser diode, and a laser. For example, the
light source can include an array of light emitting diodes.
[0020] The detection system can be a spectral detection system, the
first and second sets of data can include first and second sets of
spectra, and the processor can remove spectral information
characteristic of the overlying features from the first set of
spectra using the first and second sets of spectra to produce
corrected spectral information representing the internal target.
The spectral detection system can include a spectrometer configured
to receive light and to generate sets of spectra from the received
light. Alternatively, the spectral detection system can include a
first spectrometer configured to receive light from the first
receiving port and to generate the first set of spectra, and a
second spectrometer configured to receive light from the second
receiving port and to generate the second set of spectra.
[0021] The processor can be configured to remove spectral
information from the first set of spectra that is characteristic of
variations in optical scattering properties of the internal target
that are unrelated to an analyte of interest therein. The processor
can be configured to remove spectral information from the corrected
spectral information representing the internal target that is
characteristic of variations in optical scattering properties of
the internal target that are unrelated to an analyte of interest
therein. The processor can be configured to remove spectral
information characteristic of the overlying features from the first
set of spectra using the first and second sets of spectra according
to the equation
{circumflex over (R)}.sub.ort=R.sub.sfm-{tilde over
(R)}.sub.sfw.sup.T
where R.sub.sfm is a spectrum from the first set of spectra, {tilde
over (R)}.sub.sf is a spectrum from the second set of spectra, w is
a weight, "T" denotes a matrix transpose operation, and {circumflex
over (R)}.sub.ort comprises corrected spectral information
representing the internal target. The processor can be further
configured to normalize the first and second sets of spectra with
respect to one another prior to producing the corrected spectral
information.
[0022] The processor can be configured to remove spectral
information that is characteristic of variations in optical
scattering properties of the internal target from the first set of
spectra by orthogonalizing the first set of spectra with respect to
a set of loading vectors of principal components determined from a
set of spectra from a plurality of samples. The plurality of
samples can have a property of the internal target within a
selected range. For example, the property of the internal target
can be a value of an analyte such as pH, hematocrit, tissue
oxygenation, or another property. The range can be selected to
facilitate correction and/or analysis of the spectra. For example,
a selected range for pH can be 7.37.+-.0.001 pH units
[0023] The processor can be configured to orthogonalize the first
set of spectra by performing a set of steps that include: (a)
performing a principal component analysis on a set of calibration
spectra to determine a set of loading vectors corresponding to
principal components of the calibration spectra; (b) determining
one or more orthogonalization factors from the principal component
analysis; (c) forming a loadings matrix having at least one
dimension equal to the number of orthogonalization factors; and (d)
orthogonalizing the first set of spectra with respect to the
loadings matrix.
[0024] In another aspect, the invention features methods for
correcting information corresponding to an internal target within a
sample measured by a system having a light source, a detection
system, and a set of at least first, second, and third light ports
which transmits light from the light source to the sample and
receives and directs light reflected from the sample to the
detection system, where a distance between the first port and the
third port corresponds to a first detection distance and a distance
between the second port and the third port corresponds to a second
detection distance, where the first detection distance is larger
than the second detection distance, and where either (i) the first
and second ports are transmitting ports and the third port is a
receiving port, or (ii) the first and second ports are receiving
ports and the third port is a transmitting port. The methods
include: (a) illuminating the sample with one or more light ports
of the set; (b) detecting the reflected light with the detection
system; (c) generating a first set of data corresponding to the
first detection distance and including information corresponding to
both an internal target within the sample and features overlying
the internal target, and a second set of data corresponding to the
second detection distance and including information corresponding
to features overlying the internal target; and (d) removing
information characteristic of the overlying features from the first
set of data using the first and second sets of data to produce
corrected information representing the internal target.
[0025] Embodiments of the methods can include any of the following
features.
[0026] The detection system can be a spectral detection system, the
first and second sets of data can include first and second sets of
spectra, and removing information characteristic of the overlying
features from the first set of data can include removing spectral
information characteristic of the overlying features from the first
set of spectra using the first and second sets of spectra to
produce corrected spectral information representing the internal
target. Removing spectral information characteristic of overlying
features of the sample from the first set of spectra can include
combining the first and second sets of spectra according to the
equation
{circumflex over (R)}.sub.ort=R.sub.sfm-{tilde over
(R)}.sub.sfw.sup.T
where R.sub.sfm is a spectrum from the first set of spectra, {tilde
over (R)}.sub.sf is a spectrum from the second set of spectra, w is
a weight, "T" denotes a matrix transpose operation, and {circumflex
over (R)}.sub.ort includes corrected spectral information
representing the internal target.
[0027] The methods can further include normalizing the first and
second sets of spectra relative to one another prior to producing
the corrected spectral information. Normalizing the sets of spectra
can include applying a polynomial fit between the first and second
sets of spectra. Coefficients used in the polynomial fit can be
derived from first and second sets of spectra recorded from one or
more reflectance standards.
[0028] The methods can include processing the first set of spectra
to remove spectral information characteristic of variations in
optical properties of the internal target that are unrelated to an
analyte of interest therein. The methods can include processing the
corrected spectral information representing the internal target to
remove spectral information that is characteristic of variations in
optical scattering properties of the internal target that are
unrelated to an analyte of interest therein. Removing spectral
information characteristic of variations in optical properties of
the internal target that are unrelated to an analyte of interest
therein can include orthogonalizing the first set of spectra with
respect to a set of loading vectors of principal components
determined from a set of calibration spectra.
[0029] Orthogonalizing the first set of spectra with respect to a
set of loading vectors can include: (a) performing a principal
component analysis on a set of calibration spectra to determine a
set of loading vectors corresponding to principal components of the
set of calibration spectra; (b) determining one or more
orthogonalization factors from the principal component analysis;
(c) forming a loadings matrix having at least one dimension equal
to the number of orthogonalization factors; and (d) orthogonalizing
the first set of spectra with respect to the loadings matrix.
[0030] In another aspect, the invention features methods of
measuring an analyte in a subject by: (a) generating a set of
corrected spectra based on reflectance measurements from an animal
according to the methods disclosed herein; (b) developing one or
more calibration equations based on a relationship between a
measurement of the analyte in the animal and the set of corrected
spectra from the animal; (c) generating a set of corrected spectra
based on reflectance measurements from the subject according to the
methods disclosed herein; and (d) determining a value of the
analyte in the subject based on the one or more calibration
equations and the set of corrected spectra from the subject. The
subject can be a human, for example. The sets of corrected spectra
can be generated based on reflectance measurements from the animal
and the subject are further processed to remove spectral
information characteristic of variations in optical properties of
internal targets comprising the analyte.
[0031] The invention also features methods of measuring an analyte
in a subject by: (a) generating a set of corrected spectra based on
reflectance measurements from a first body site of the subject
according to the methods disclosed herein; (b) developing one or
more calibration equations based on a relationship between a
measurement of the analyte at the first body site and the set of
corrected spectra from the first body site; (c) generating a set of
corrected spectra based on reflectance measurements from a second
body site of the subject according to the methods disclosed herein;
and (d) determining a value of the analyte at the second body site
based on the one or more calibration equations and the set of
corrected spectra from the second body site. The subject can be a
human, the first body site can be an arm, and the second body site
can be a leg. The sets of corrected spectra generated based on
reflectance measurements from the first and second body sites can
be further processed to remove spectral information characteristic
of variations in optical properties of internal targets comprising
the analyte.
[0032] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Although
methods and materials similar or equivalent to those described
herein can be used in the practice or testing of the present
invention, suitable methods and materials are described below. All
publications, patent applications, patents, and other references
mentioned herein are incorporated by reference in their entirety.
In case of conflict, the present specification, including
definitions, will control. In addition, the materials, methods, and
examples are illustrative only and not intended to be limiting.
[0033] Other features and advantages of the invention will be
apparent from the following detailed description, and from the
claims.
DESCRIPTION OF DRAWINGS
[0034] FIG. 1 is a schematic diagram of a spectrometer system
described herein.
[0035] FIG. 2 is a top schematic view of an optical bench and
selected components of the spectrometer system of FIG. 1.
[0036] FIG. 3 is a schematic diagram of an incandescent lamp.
[0037] FIG. 4A is schematic diagram of an arrangement of two fiber
optic cables for delivering light to a sample and a fiber optic
cable for delivering reflected light from the sample to a
spectrograph.
[0038] FIG. 4B is a schematic end view of an arrangement of fibers
in the fiber optic cable for delivering light to the sample and
fibers in the fiber optic cable for delivering reflected light from
the sample to a spectrograph.
[0039] FIG. 5A is a schematic side view of an embodiment of a probe
head for delivering light to a sample and for receiving reflected
light from the sample.
[0040] FIG. 5B is a schematic bottom view of an embodiment of a
probe head with integrated light sources.
[0041] FIG. 6A is a schematic top view of a light shield for
holding and shielding the probe head shown in FIG. 5.
[0042] FIG. 6B is a schematic top view of an alternative embodiment
of the light shield of FIG. 6A.
[0043] FIG. 7 is a schematic view of a shutter used in the
spectrometer system of FIG. 1.
[0044] FIG. 8 is a circuit diagram of an electrical circuit for
controlling the shutter shown in FIG. 7.
[0045] FIGS. 9A-9E are graphs that show results of a correction to
remove spectral interference due to overlying layers from
reflectance spectra measured from a human subject.
[0046] FIGS. 10A-10E are graphs that show results of a correction
to remove spectral interference due to overlying layers from
reflectance spectra measured from another human subject.
[0047] FIG. 11 is a graph showing values of maximum heme absorbance
versus measured blood hematocrit for uncorrected spectral data.
[0048] FIG. 12 is a graph showing values of maximum heme absorbance
versus measured blood hematocrit for spectral data corrected for
effects due to overlying skin and fat layers.
[0049] FIG. 13 is an image of an embodiment of a near-infrared
reflectance spectrometer system.
[0050] FIG. 14 is a graph showing a set of uncorrected spectral
absorbance measurements from different human subjects.
[0051] FIG. 15 is a graph showing a set of spectral absorbance
measurements from different human subjects after application of a
PCA loading correction algorithm.
[0052] FIG. 16 is a graph showing predicted pH versus measured pH
for a PLS model of pH based on uncorrected spectral absorbance
data.
[0053] FIG. 17 is a graph showing predicted pH versus measured pH
for a PLS model of pH based on spectra absorbance data corrected
using a PCA loading correction method.
[0054] FIG. 18 is a graph showing an aqueous absorption spectrum
for the dye ADS780WS.
[0055] FIG. 19 is a graph showing short distance absorption spectra
for a set of tissue-like phantom samples.
[0056] FIG. 20 is a graph showing long distance absorption spectra
for a set of tissue-like phantom samples.
[0057] FIG. 21 is a graph showing long distance absorption spectra
for a set of tissue-like phantom samples after short-distance
corrections have been applied.
[0058] FIG. 22 is a graph showing uncorrected spectral absorbance
measurements from a set of tissue-like phantom samples.
[0059] FIG. 23 is a graph showing spectral absorbance measurements
from a set of tissue-like phantom samples after short-distance
corrections have been applied.
[0060] FIG. 24 is a graph showing spectral absorbance measurements
from a set of tissue-like phantom samples after SNV scaling
corrections have been applied.
[0061] FIG. 25 is a graph showing spectral absorbance measurements
from a set of tissue-like phantom samples after PCA loading
corrections have been applied.
[0062] FIG. 26 is a graph showing spectral absorbance measurements
from a set of tissue-like phantom samples after short-distance
corrections and SNV scaling corrections have been applied.
[0063] FIG. 27 is a graph showing spectral absorbance measurements
from a set of tissue-like phantom samples after short-distance
corrections, SNV scaling corrections, and PCA loading corrections
have been applied.
[0064] FIG. 28 is a graph showing prediction results for a PLS
model of dye concentration based on uncorrected spectral absorbance
data.
[0065] FIG. 29 is a graph showing prediction results for a PLS
model of dye concentration based on spectral absorbance data
corrected using three different correction methods.
[0066] FIG. 30 is a graph showing spectral absorbance data recorded
from different subjects at similar pH levels, and corrected using
short-distance and SNV scaling correction methods.
[0067] FIG. 31 is a graph showing the spectral absorbance data of
FIG. 30, further corrected using PCA loading correction
methods.
[0068] FIG. 32 is a graph showing spectral absorbance data recorded
from different subjects at different pH values and corrected using
short-distance and SNV scaling correction methods.
[0069] FIG. 33 is a graph showing the spectral absorbance data of
FIG. 32, further corrected using PCA loading correction
methods.
[0070] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION
General Methodology
[0071] As described herein, a new fiber optic sensor and associated
processor programmed and configured to perform certain mathematical
algorithms are used to extend the applicability of NIRS for the
noninvasive measurement of blood and tissue chemistry. The sensor
is designed and used to remove the interfering spectral influence
of one or more tissues that are not of interest, such as skin and
fat, that overlay tissues of interest and complicate measured
signals from those tissues, such as scattering from muscle tissue
and/or from tissue of an organ in a patient, thus permitting
greater generalization of chemometrics-based calibration equations.
In particular, the sensor has a detection port for detecting light
scattered and reflected from a patient and at least two
illumination ports that are located at different distances from the
detection port. Light from an illumination port that is close to
the detection port causes a reflectance spectrum that is due mainly
to the overlying fat and skin layers, while light from an
illumination port that is more distant from the detection port
causes a reflectance spectrum that is due to the combination of
reflections from the overlying skin and fat layers as well as
deeper muscle and/or organ layers. Information in the two spectra
and generalized chemometrics-based calibration equations can be
used to extract chemical information about just the tissue of
interest, e.g., the underlying muscle and/or organ layer.
[0072] The sensor can also be used to reduce and/or remove
interfering spectral influences that arises due to sample-to-sample
structural variations in tissues of interest. For example, the
sensor can be used to measure a particular analyte in one or more
muscle tissue layers that underlie skin and fat layers. After
removing the spectral influence of the skin and fat layers from
measured reflectance spectra, additional steps can be performed by
the sensor to remove spectral influences that arise from variations
in optical properties of the muscle tissue. For example, optical
properties of the muscle tissue can vary according to a surface
texture, and/or a capillary density, and/or a fiber structure,
and/or other structural properties of the muscle tissue. Variations
in optical properties of the tissues of interest typically affect
an optical scattering coefficient of the tissue.
[0073] By generalizing the calibration equations based on corrected
reflectance spectra, the sensor can be used to perform accurate
measurements when placed on muscle or other tissue sites different
from those used for calibration development, and calibration
equations developed from animal models of pathophysiology can be
applied to human subjects with clinically acceptable results. The
methods can also be applied during clinical measurement to
continually correct for varying patient conditions that will alter
spectra, such as skin blood flow and surface changes which occur
during wound healing.
[0074] The new devices and techniques described herein are aimed at
reducing spectral interference from overlying tissue such as skin
and fat on the spectra measured from underlying muscle and/or
organs. When successfully implemented, calibration equations
developed using these devices and techniques can be applied to
spectra acquired from almost any anatomical site, irrespective of
the optical properties of the overlying layer(s). Further, the new
devices and techniques are aimed at reducing spectral interference
from structural variations in tissues of interest that are not
correlated to measurements of a specific analyte within the tissues
of interest. Calibration equations developed based on spectra that
are corrected for structural variations in tissues of interest,
e.g., variations that affect the optical properties of tissues of
interest, can be applied to different measurement regions on a
single measurement subject, and/or to different measurement
subjects. For example, calibration equations developed based on
measurements performed on a patient's arm, which has a thin fat and
skin layer, can be applied to spectra collected from the patient's
leg, which has a thicker fat and skin layer.
[0075] With a powerful light source and a sensitive detector, the
devices and techniques described herein allow accurate spectra to
be collected from internal organs by reducing spectral interference
from overlying skin, fat, and muscle layers, and from structural
variations in the internal organ tissues. Another advantage of the
new approaches is the ability to continually compensate for
changing patient conditions such as variation in skin blood flow
and surface texture. By removing spectral interference from the
overlying layers from the spectra of the underlying layers,
variations in the overlying layer spectra will cause little
variation in the measured information of the underlying layers
obtained from the reflectance spectra. By removing spectral
interference from structural variations in tissues of interest
(e.g., the underlying layers), variations in the structure of the
tissues of interest will contribute only relatively small
variations to measured information for selected analytes in the
tissues of interest. These spectral interference corrections permit
a more accurate determination of important blood and tissue
chemistry parameters from patients.
[0076] In-vivo calibration requires access to patients who have
variation in the analyte to be measured and offer the same or
similar pathophysiology as expected in future application of the
devices and techniques. Often, it is difficult to obtain access to
a sufficient number of subjects and even when they are available,
the variation may not be large enough to encompass the entire
pathophysiological range for the parameter that is sought to be
modeled and measured. Another issue is how to incorporate the
potential spectral interference from new treatment methods. For
instance, when developing methods of measuring muscle pH to help
guide resuscitation of a patient from shock, it may be necessary to
update the calibration equation used for determining muscle pH to
account for new therapeutic agents used when the patient is in
shock. However, deliberately placing human test subjects in shock
to develop updated calibration equations is not possible. A
superior method for developing robust calibration equations that
span large ranges of medical parameters and that can be altered for
new conditions and drugs would be to develop calibration equations
on animals and transfer them directly to humans. By using animals
it is possible, for instance, to observe variations in analyte
values caused by serious shock. This can be done on multiple
animals to validate the sensor for this application before it is
used on critically ill humans. Also some analytes are difficult to
vary reliably in a clinical setting. However, the methods described
herein will allow calibration equations developed on animals to be
transferred successfully for use on human subjects. Once the
spectral influence of the overlying tissue layers are removed, the
muscle spectra of animals, such as swine, may be similar to those
of human subjects. Spectral differences in human and animal muscle
structure can be corrected using methods that remove these
variations. Calibration equations derived from swine muscle can
then be used for human subjects.
Overall System
[0077] As shown in FIG. 1, a portable, fiber-optic-based
spectroscopic system 100 for measurement of reflectance spectra
from a sample 102 located remotely from the system includes a lamp
104, a power supply for the lamp 106, an optical bench 108, a
shutter system 110, a driver for the shutter system 112, a
spectrograph 114, a fiber optic cable 116, and a computer 118.
Light from the lamp is manipulated by optics within the optical
bench 106 and can be controlled by a shutter system 110 that is
driven by a shutter driver 112. Light can be passed selectively by
the shutter system 110 into a first fiber optic cable 116a or a
second fiber optic cable 116d that guide the light to the sample
102 to illuminate the sample.
[0078] When light is guided to the sample in the fiber optic cable
116a or 116d, light is reflected from the sample 102 and collected
by a third portion of the fiber optic cable 116c that guides the
reflected light from the sample 102 to the spectrograph 114. The
reflected light is analyzed by the spectrograph 114 to gather
information about the sample 102.
[0079] The spectrograph 114 can be a commercial portable
spectrograph that can be operated by computer control. For example,
an Ocean Optics USB2000 spectrograph with a grating optimized for
performance in the wavelength range of 500-1000 nm can be used. The
spectrograph detector can be a 2048 element shallow-well linear
CCD-array. The spectrograph can be equipped with a 200 micron wide
slit to increase resolution, a collection lens to increase light
collection efficiency at the detector, and a long pass filter to
block light with a wavelength less than 475 nm from reaching the
detector. The USB2000 spectrograph interfaces with the computer
118, e.g., through either a USB or RS232 port.
[0080] The system 100 further includes an on-board computer 118 for
controlling the shutter driver 112, the spectrograph 114, and for
processing, storing, and displaying data from the spectrograph.
[0081] As shown in FIG. 2, optical bench 108 includes several
primary optical components of the system. An illumination lamp 104
provides light to illuminate the sample. A first optical connector
212 couples the light to a first fiber-optic cable 116a for
carrying light to illuminate the sample 102. A second optical
connector 214 couples light to a second fiber-optic cable 116d that
carries light to illuminate the sample 102. A shutter 250 can
select whether of the first fiber-optic cable 116a or the second
fiber optic cable 116d is illuminated by the lamp light. Optical
bench 108 is used to set up and maintain proper alignment of the
above-mentioned optical components to enhance the accuracy and
reproducibility of the system 100 as a reflectance spectroscopy
measurement system. Optical bench 108 can be fabricated from
aluminum because aluminum can be easily machined to close
tolerances and has high thermal conductivity to promote heat
dissipation and minimize thermal stress and distortion on the
components of the system 100.
[0082] Lamp 104 can be a white light source (e.g., a
tungsten-halogen 9 W bulb such as a Welch-Allyn 8106-001 bulb) that
is driven by a specially designed power supply 106 to allow for
fast ramp-up and stable operation of the lamp. The lamp 104 can be
a continuous wave ("cw") light source or a pulsed light source. The
lamp 104 is housed within its own machined reflector, so that it is
relatively easy to replace when necessary, and its optical
alignment is assured through the design of the optical bench. The
lamp rests against mechanical stops that ensure that it is
accurately located with respect to the fiber optic cables 116a and
116d. Light from the lamp 104 can focused down a center axis of the
optical bench 108 by a rear reflector 220 (e.g., an ellipsoidal
reflector).
[0083] The general lamp configuration is illustrated in FIG. 3,
which shows an incandescent light source 300 that includes an
electrically resistive filament 302 within a transparent bulb 304.
Filament 302 is generally made of tungsten. Bulb 304 can be made of
glass, quartz, or other materials and can be evacuated or can be
filled with a halogen gas, an inert gas, or a mixture of gases.
Electrical current is supplied from a power supply 306 to the
filament 302 through electrically conductive lead wires 308 that
are electrically connected to a base 310 of the light source. The
electrical current causes the filament to radiate as a black body.
The power supply 306 can supply direct current (DC) or alternating
current (AC). To ensure stable operation of the lamp 104 and a
constant spectrum emitted from the bulb, the filament is driven by
an electrical circuit that supplies current to the filament such
that the temperature of the filament remains substantially
constant, such that a stable blackbody radiation spectrum is
emitted from the bulb.
Fiber Optic Cable System
[0084] Referring again to FIG. 1, light from the light source 104
can be used to excite an electromagnetic reflectance spectrum of
the sample 102. Light can be ported from light source 104 to the
sample 102 in fiber optic cables 116a and/or 116d to illuminate the
sample and to excite the sample optically. Light reflected from the
sample 102 can be collected and delivered from the sample in a
fiber optic cable 116c to a spectrograph 114 that measures the
reflectance spectrum of the sample 102. Illumination light in cable
116a exits the cable and is incident on sample 102 at a first
distance (e.g., about 32 mm) from the entrance to the cable 116c
that collects reflected light from the sample and guides the
reflected light to the spectrograph 114 for analysis. Illumination
light in cable 116d exits the cable and is incident on sample 102
at a second distance (e.g., about 2.5 mm) from the entrance to the
cable 116c that is less than the first distance. Spectra from the
sample that are collected when the sample is illuminated by light
from cable 116a and from cable 116d can be used to extract detailed
information about the sample, as explained below.
[0085] Referring to FIG. 2, the fiber-optic illumination cable 116a
for illuminating the sample 102 can be positioned directly in front
of the lamp 104 with the end of the fiber-optic cable 116a being at
the focal point of the light emitted from the lamp 104. The cable
116a is inserted into position before system use, and a mechanical
click stop ensures that the cable 116a is properly positioned and
secured in relation to the lamp 104 and the reflector 220.
[0086] A second fiber optic cable 116d that also is used for
illuminating the sample 102 is threaded into port 214 in the
optical bench, e.g., at an angle to an optical axis of reflector
220 of between about 5 degrees and about 90 degrees (e.g., between
about 10 degrees and about 60 degrees, between about 15 degrees and
about 35 degrees) with respect to the focused beam axis. Placing
the cable 116d into port 214 at an angle to the focused beam axis
results in a reduced intensity of light entering the cable 116d,
compared to an on-axis position of cable 116d. However, because
cable 116d delivers light to the sample at a closer distance to
detection cable 116c than cable 116a does, the amount of light
reflected by the sample and collected in detection cable 116c can
be similar whether the sample is illuminated with the higher
intensity light from cable 116a or the lower intensity light in
cable 116d.
[0087] As shown in FIG. 4A, the fiber optic cable can include
different fiber bundles 116a, 116c, and 116d. The cable holding the
sample bundles 116a or 116d can be contained in a common protective
sheath with the cable holding the return bundle 116c outside the
unit 100 housing the spectrograph 114 and the light source 104. The
sample bundles 116a, 116c, and 116d can be gathered together
outside the housing of the system 100, which contains spectrograph
114 and the lamp 104, about 8-12 inches downstream from the
housing. The cable containing the bundles 116a, 116c, and 116d
connects spectrograph 114 and the lamp 104 to a probe head 400 that
can be located at sample 102 to perform reflectance measurements on
sample 102.
[0088] As shown in FIG. 4B, at the sample end of the cable system,
a probe head 400 includes illumination bundles 116a and 116d, which
are oriented perpendicular to a face of the housing 400 and
perpendicular to a surface of sample 102. Housing 400 can hold the
ends of illumination fiber bundle 116a, illumination fiber bundle
116d, and detection fiber bundle 116c. Fiber bundle 116a can
contain approximately 2666, 50 .mu.m glass fibers with a numerical
aperture (NA) of about 0.66. The detection bundle of fibers in
return cable 116c returns reflected light from the sample to the
spectrograph 114, and in one embodiment can contain 109, 50 .mu.m
glass fibers with a NA of about 0.66. At the sample end of the
fibers, the fibers are oriented perpendicularly to sample 102.
Along the length of the fiber optic cable, between light source 104
and spectrograph 114 at one end and the sample 102 at the other
end, the sample illumination bundle 116a that delivers light to the
sample is optically shielded from the return bundle 116c with an
opaque material, such as an opaque tape or plastic sheath or tube,
so that any light that leaks out of illumination bundles 116a
and/or 116d is not coupled into the return bundle 116c. To improve
optical coupling between lamp 104 and sample fiber bundles 116a and
116d, a tapered NA converter can be placed at the end of sample
cable bundles 116a and 116d to convert the 0.42 NA of the light
source to the 0.66 NA of the fibers. This increases the collection
efficiency into the fibers by about 15%.
[0089] To reduce the NA of the return fiber bundle to 0.22 as may
be needed to ensure proper interfacing with spectrograph 114, a 600
.mu.m diameter fused silica rod can be placed at the end of fiber
bundle 116c. To prevent stray light from entering the spectrograph
114, a black, light-absorbing epoxy or other material can be used
to surround the silica rod.
[0090] As shown in FIG. 5, light can be delivered from the unit 502
housing the lamp 104 and the spectrograph 114 to the probe head 400
that delivers light to sample 102 in a fiber bundle 116a and/or
116d, and light scattered from the sample can be collected and
routed back to spectrograph 114 in fiber bundle 116c. The bundles
116a, 116c, and 116d can be contained in a single cable that runs
between housing unit 502 and probe head 400, and the cable can be
several meters long to allow convenient access to and data
collection from a sample or a patient. To minimize cross talk
between light illumination bundles 116a and 116d and light
detection bundle 116c, bundles 116a and 116d delivering
illumination light from lamp 104 to sample 102 can be wrapped in
black tape or other opaque material so that any leaked light does
not couple into light return bundle 116c.
[0091] As shown in FIG. 5, probe head 400 can have an illumination
light port 506 for delivering light to the sample that is spatially
separated by a distance (SD).sub.2 from a detection light port 508
for receiving light from the sample. Fiber bundle 116a can be
coupled horizontally into probe head 400, and light from bundle
116a can be reflected from a 45 degree mirror 510 within the probe
head and directed vertically towards sample 102 from light port
506. Similarly, light scattered from sample 102 can be collected by
light port 508 and reflected by a mirror 510 into detector fiber
bundle 116c. Alternatively, fiber bundles 116a and 116c can enter
probe head 400 horizontally and then bend 90 degrees in a vertical
direction such that they are directly coupled to light ports 506
and 508, respectively.
[0092] Illumination light port 506 can contain a 3.5 mm bundle of
50 .mu.m glass fibers having an overall NA of 0.66 for directing
light from housing unit 502 to sample 102. Sample 102 can include a
top layer (e.g., a skin layer) 102s, an overlying layer (e.g., a
fat layer) 102f, and an underlying layer (e.g., a muscle layer)
102m, and light can be reflected from any or all of the layers to
the detector fiber bundle 116c. Detection light port 508 that is
coupled to bundle 116c is spaced a distance of between about 10 mm
and about 100 mm (e.g., between about 20 mm and about 50 mm,
between about 30 mm and about 40 mm, between about 30 mm and about
32 mm) from illumination light port 506, and directs diffusely
reflected light from the sample to unit 502 housing spectrograph
114, where the reflected light can be analyzed. Light port 508 can
have a 1 mm diameter detection bundle of 50 .mu.m glass fibers with
a collective NA of 0.66.
[0093] Probe head 400 can include an additional illumination light
port 514 configured similarly to first illumination light port 506,
but located closer to detection light port 508 than first
illumination light port 506, at a distance (SD).sub.1. The distance
(SD).sub.1 can be between about 1 mm and about 5 mm (e.g., about
1.5 mm, about 2.5 mm, about 3 mm, about 4 mm). For example, the
center of the 1 mm fiber bundle of second illumination light port
514 can be located about 2.5 mm from the center of the 1 mm
diameter fiber bundle of detection light port 508, while the 3.5 mm
diameter fiber bundle of first illumination light port 506 can be
located about 30 mm from the center of detection light port 508. A
diameter of light port 514 can be smaller than a diameter of light
port 506, so that, although more light is incident on sample 102
from the light port 506 than from light port 514, approximately the
same reflected intensity is collected by detection light port 508
regardless of whether the sample is illuminated with light from
illumination port 506 or 514. Having similar intensities in the
reflected spectra whether the sample is illuminated with light from
illumination port 506 or 514 permits a good signal-to-noise ratio
to be obtained without having to integrate on the detector for a
longer time when light from one of the two illumination ports is
used.
[0094] The respective spacings between the two illumination light
ports 506 and 514 and the detection light port 508 ((SD).sub.1 and
(SD).sub.2) are chosen in conjunction with the intensity and
spectrum of the light emitted from illumination light ports 506 and
514 and the reflectance spectrum of sample 102 to obtain particular
information about sample 102. For example, spectroscopic system 100
can be used to direct near infrared radiation (e.g., radiation
having a wavelength of 700-1000 nm) through a human patient's skin
to permit direct, noninvasive measurement of blood chemistry or
chemistry in tissue beneath the skin without removing a blood or
tissue sample from the patient. In particular, system 100 can be
used to measure muscle pH, muscle oxygen tension (PO.sub.2), and
blood hematocrit from continuous wave near infrared spectra
obtained with the system. Shorter distance (SD).sub.1 should be
selected to properly induce reflection only from tissue that is not
of interest, such as skin and fat overlying a tissue of interest,
such as muscle or tissue of an organ. As indicated below, this
distance has been calculated for overlying skin and fat, where
muscle is the tissue of interest.
[0095] To record spectral information from a human patient,
thermally conducting feet 520 of probe head 400 are placed in
contact with a portion of the patient's body, light is directed
from illumination ports 506 and/or 514 to the patient, and
reflected light is collected in detection port 508. For example,
feet 520 of probe head 400 can be placed in contact with the skin
on the surface of a patient's forearm, so that light can be emitted
from illumination light ports 506 and/or 514, reflected from tissue
within a portion of the patient's body (e.g., the patient's hand,
forearm, calf, thigh, stomach, or chest) and collected via
detection light port 508. To record information from the patient's
muscle, light from illumination light ports 506 and/or 514
penetrates through skin layer 102s and fat layer 102f, which is
generally between about 3 mm and about 10 mm thick (e.g., between
about 4 mm and about 5 mm thick), to reach muscle layer 102m, and
then the light is scattered from the muscle layer and collected in
detection light port 508. In some embodiments, second illumination
light port 514 and detection light port 508 can be located together
in one thermally conducting foot 520, e.g., in embodiments
involving detection of analytes in human muscle.
[0096] Using lamp 104, e.g., an 8 Watt lamp, about 7 lumens of
light can be emitted from a 3.5 mm diameter illumination light port
506 or 514, or about 25 lumens of light can be emitted from a 6 mm
diameter illumination light port. It has been determined
empirically that a spacing of about 30 mm between illumination
light port 506 and detection light port 508 allows light collected
in detection light port 508 to include a significant signal due to
light scattered from muscle tissue 102m underlying skin and fat
layers 102s and 102f. When light is emitted from second
illumination light port 514 that is positioned closer to detection
light port 508 than first illumination light port 506, the light
collected in detection light port 508 includes a significant signal
from light scattered from the overlying skin and fat layers 102s
and 102f. This second signal can be used to remove signal
components that arise due to scattering and/or absorption from the
overlying skin/fat layers 102s and 102f in the overall signal
recorded when the patient's arm is illuminated with light from
first illumination light port 506. Light emitted from the two
illumination light ports 506 and 514 can originate from the same
lamp 104, and shutter 250 can be used to control emission of light
from each of first illumination light port 506 and second
illumination light port 514, as explained in more detail below.
[0097] Two signals corresponding to light scattering from shallow
skin/fat layers 102s and 102f and from deeper muscle layer 102m can
also be obtained by using a single illumination light port and two
light detection ports, one of which is located closer to the
illumination light port than the other.
[0098] Probe head 400 and its feet 520 can be made of a
thermally-conductive material (e.g., aluminum or copper) to conduct
heat away from a patient's skin. If the probe head is made of
non-conductive material (e.g., plastic), heat delivered though
illumination light ports 506 and/or 514 can be sufficient to dilate
blood vessels in the skin and alter skin blood volume of the
patient. This effect of heat on skin blood flow can change a
reflectance spectrum recorded from the patient. A thermal conductor
in probe head 400 provides a thermal bridge between feet 520 at
opposite ends of probe head 400, so that the temperature of a
patient's tissue is substantially the same in the vicinity of the
two illumination ports 506 and 514 and detection port 508.
[0099] Referring to FIG. 5B, in some embodiments, the light source
can include one or more light-emitting diodes. Probe head 400
includes a first light source 580 positioned within probe head 400
to form an illumination port at a distance (SD).sub.2 from
detection port 584. Probe head 400 also includes a second light
source 582 positioned within probe head 400 to form an illumination
port at a distance (SD).sub.1 from detection port 584. Light
sources 580 and 582 do not include fiber optic cables or other
coupling elements, and are positioned such that light from each of
the sources illuminates a sample directly. Light reflected by the
sample is received by the system via detection port 584.
[0100] Light sources 580 and 582 can each include an array of light
emitting diodes (LEDs). A number and spatial distribution of the
LEDs can be selected to provide a particular reflected light
intensity in detection port 584 from each source. For example,
light source 580 can include a relatively densely packed array of
LEDs, and light source 582 can include a less densely packed array
of LEDs, so that the reflected light received in detection port 584
from each of the two sources is approximately equal in
intensity.
[0101] LEDs used in light sources 580 and/or 582 can provide light
in varying regions of the electromagnetic spectrum. For example,
LEDs can provide light having wavelengths in the visible and/or
ultraviolet and/or near-infrared and or infrared regions of the
spectrum. LEDs can also provide light in other regions of the
spectrum, and in multiple regions of the spectrum at the same time.
In some embodiments, multiple different types of LEDs having
different light emission properties (e.g., wavelength, intensity,
and other properties) can be provided in a single light source,
such as light source 580 and/or light source 582.
[0102] In certain embodiments, light sources 580 and/or 582 can
include other types of sources (e.g., incandescent sources,
laser-based sources) integrated in probe head 400 or coupled to
probe head 400. Detectors such as spectrometers can be incorporated
into probe head 400, or can be optically coupled to probe head 400,
e.g., using a fiber optic cable.
[0103] Referring to FIGS. 6A and 6B, the probe head 400 can be
located in proper position by a light shield 600. Light shield 600
has an opening 602 through which a foot 520 of probe 400 fits, an
opening 604 though which illumination light port 506 fits, and an
opening 606 through which a second foot 520 fits. The second foot
can house illumination light port 514 fit and detection light port
508. Thus, the light shield locates the feet 520 and the
illumination and detection light ports 506, 514, and 508 of probe
head 400 in a fixed position. Light shield 600 includes an opaque
material that shields the patient's body from stray light, such
that only light from illumination light ports 506 and/or 514
reaches the patient, and such that light collected in detection
light port 508 is due only to light having a known spectrum that
emerges from illumination light port 506. Light shield 600 can
extend about 3.5 cm in all directions from the detection light port
508 to shield the detection port from stray light. With probe head
400 positioned in the light shield 600, the probe head can be
positioned against the patient's body, for example, by taping,
strapping, or sticking the light shield against the patient's body.
For example, probe head 400 can be held against light shield 600
with double-sided adhesive, which allows probe head 400 to contact
the patient's skin without excessive pressure that could alter the
blood volume under the probe head.
[0104] As shown in FIG. 6A, in some embodiments, the light shield
can be made in one piece having a main part 601 that has the
various openings or apertures, and a cable management part 603
connected to the main part 601 by a narrow connecting region 605.
In other embodiments, as shown in FIG. 6B, the light shield has a
two-piece design in which the cable management part 603 is separate
from the main upper part 601. This arrangement enables the two
parts to be separated by a distance greater than the length of
connecting region 605. In many of these embodiments, the light
shield cable management part 603 typically includes a clip 608 or
other mechanism that can secure a cable that contains the fiber
bundles 116a, 116c, and 116d that transport light between
spectrometer unit 502 and probe head 400. Clip 608 secures the
cable, so that the weight of the cable does not displace the probe
head from its location on a patient. The shield 600 can be made
from plastic, for example, or from another lightweight opaque
material. The light shield can also be made from a
thermally-conductive material, such as a thin sheet of copper or
aluminum to assist in the dissipation of excess heat, so that the
heat is not transferred to the patient's skin.
Optical Bench and Shutter System
[0105] The optical bench can include a shutter system 110 for
directing the light to different legs of the fiber optic cable
system. By controlling the path of the light in the cable system,
the shutter system can be used to selectively illuminate the sample
with different illumination fibers 116a or 116d.
[0106] Referring again to FIG. 2, optical bench 108 provides a
mount for a stepper-motor 240 that actuates optical shutter 250 of
the shutter system 110. Shutter 250 is positioned between lamp 104
and two fiber optic cables 116a and 116d, and is shaped such that
it can either block or pass light to each of the two fiber optic
cables. In FIG. 2, only the top edge of the shutter 250 is shown. A
profile of opaque shutter 250 is shown in FIG. 7. Shutter 250 is
coupled to stepper motor 240 by a shaft that passes through a hole
710 in the shutter. When the shaft rotates, the shutter is rotated
about an axis passing through the center of the hole 710. A second
shaft (not shown) fixed to the optical bench 108 passes through a
second hole 720 in shutter 250 and limits the rotational motion of
shutter 250.
[0107] When shutter 250 is rotated maximally in the clockwise
direction, as shown in FIG. 7, hole 730 in the shutter is located
between lamp 104 and fiber optic cable 116a that leads to sample
102, so that light propagates through hole 730 and sample 102 is
illuminated by light emerging from fiber bundle 116a. With shutter
250 in this position, light reflected from sample 102 is collected
by cable 116c and guided to spectrograph 114. In this position, end
740 of shutter 250 blocks light from entering cable 116d.
[0108] When shutter 250 is rotated counterclockwise to a middle
position, such that the shaft passing through hole 720 is
positioned in the middle of hole 720, hole 730 is rotated out of
the beam path of the light emitted from lamp 104. Thus, light does
not pass through hole 730, and is blocked by opaque shutter 250
from entering the sample cable 116a. Light is also blocked from
entering the fiber bundle 116d by end 740 of shutter 250.
[0109] When shutter 250 is rotated maximally in the
counterclockwise direction, light does not pass through hole 720
and is blocked by the opaque shutter from entering sample cable
116a. Light passes over angled edge 750 of shutter 250 and enters
fiber bundle 116d and reaches spectrograph 114. In this position,
spectrograph 114 measures a reflectance spectrum of sample 102 when
the sample is illuminated by fiber bundle 116d.
[0110] Computer 118 controls shutter 250 to switch data acquisition
in spectrograph 114 between collecting data in a first channel
(i.e., when sample 102 is illuminated with light from fiber bundle
116a) and collecting data in a second channel (i.e., when sample
102 is illuminated with light from fiber bundle 116d). Computer 118
can control shutter 250 via a shutter driver circuit. One
embodiment of a suitable shutter driver circuit is shown in FIG. 8.
The operation of this circuit is described in U.S. application Ser.
No. 11/113,347 entitled "SPECTROMETER SYSTEM FOR OPTICAL
REFLECTANCE MEASUREMENTS", filed Apr. 25, 2005, and now published
as U.S. Publication Number 2005/0259254, the entire contents of
which are incorporated herein by reference.
[0111] Computer 118 also controls how spectrograph 114 collects
data. For example, computer 118 can control an integration time of
the spectrograph detector, a number of spectra to be averaged, and
an amount of smoothing before spectra are stored in the computer.
It is possible for computer 118 to do this independently for each
channel of data acquisition. Parameters can be chosen to maximize a
response in each of the reference channel and the sample channel
without saturating the detector.
Other Spectrometer Systems
[0112] Suitable light sources for spectrometer system 100 include
incandescent sources, e.g., lamps, light emitting diodes (LEDs),
laser-based sources, and other sources. For example, one or more
LEDs can be combined to provide light for use in making reflectance
measurements from a sample. The light provided by the various light
sources can include wavelengths in selected regions of the
electromagnetic spectrum such as the infrared and/or near-infrared
region, the visible region, the ultraviolet region, and/or other
regions of the electromagnetic spectrum, for example.
[0113] In some embodiments, spectrometer system 100 may be
configured to illuminate a sample directly, without coupling
illumination light from a light source through a fiber optic cable.
For example, the light source can be an incandescent lamp
positioned to directly illuminate a sample through an illumination
port such as an aperture. Light reflected from the sample can then
be received by one or more detectors and analyzed.
[0114] In certain embodiments, more than one detector can be used.
For example, light can be used to illuminate a sample from an
illumination port, and illumination light reflected from the sample
can be received in two detection ports. Each of the two detection
ports can be coupled to a spectrometer configured to measure a
spectrum of the reflected light, so that the detection system
includes two spectrometers operating in parallel. Spectral data can
be acquired simultaneously at each detection port, which can
provide a speed advantage relative to systems that have only a
single spectrometer. The system can operate without use of a
shutter system for blocking and unblocking illumination ports,
which can reduce the cost of the system and increase the system's
mechanical reliability.
Spectral Correction Algorithms
[0115] There are three different correction algorithms that can be
implemented. Although each correction algorithm works well
individually, any two algorithms, or all three algorithms, can be
used together to correct measured spectral data.
1. Short-Distance Corrections
[0116] Spectral contributions that arise from tissue layers
overlying tissue layers of interest (e.g., underlying tissues) can
be corrected by subtracting the contributions due to the overlying
layers from reflectance spectra that include contributions from
both the overlying and underlying layers. Reflectance spectra that
include contributions from both types of layers, and reflectance
spectra that include contributions from substantially only
overlying layers, can be measured separately using spectrometer
system 100. For example, as shown in FIG. 5, the distance
(SD).sub.2 between illumination port 506 and detection light port
508 is greater than the distance (SD).sub.1 between illumination
port 514 and detection light port 508. The distance (SD).sub.2 can
be selected such that reflectance spectra R.sub.sfm recorded when
sample 102 is illuminated with light from illumination port 506
contain spectral information about skin layer 102s, fat layer 102f,
and muscle layer 102m. The distance (SD).sub.1 can be selected such
that reflectance spectra, R.sub.sf, recorded when sample 102 is
illuminated with light from illumination port 514, substantially
contain spectral information only about skin layer 102s and fat
layer 102f. The instrument parameters, fiber size, and spectrometer
integration time of system 100 can be chosen to obtain a high
signal-to-noise ratio in the reflectance spectra for the dynamic
range of the system for each illumination port and detection
port.
[0117] Before subtracting out spectral information contained in the
R.sub.sf spectra that is due to overlying layers from the spectrum
R.sub.sfm that includes information about both the overlying and
underlying tissue layers, the R.sub.sf spectra can be normalized to
the R.sub.sfm spectra, so that the spectra share a common
measurement space. To transform R.sub.sf into the measurement space
of R.sub.sfm, a photometric mapping of the R.sub.sf spectra to the
R.sub.sfm spectra is performed. First, reflectance spectra are
recorded from three or more optically homogenous reflectance
standards having reflectivity values ranging from 2% to 99% when
light is incident on sample 102 from illumination port 506 and from
illumination port 514. Next, the reflectance spectrum for each
reference standard is estimated by dividing each measured intensity
spectrum by the spectrum recorded for the 99% reference standard.
For example, the estimated reflectance, R, of the 50% standard is
50/99 (i.e., 50.5%). Likewise, the estimated reflectance of the 99%
standard is 99/99 (i.e., 100%). Finally, a wavelength-specific
polynomial model correlating spectra, R.sub.sf, recorded when the
standard is illuminated with light from second light port 514 with
spectra, R.sub.sfm, recorded when the standard is illuminated with
light from first light port 506. Each wavelength-specific model is
used to adjust the scale of the reflectance estimates from R.sub.sf
to R.sub.sfm. This procedure corrects for differences in light
throughput and collection efficiency when the different
illumination ports 506 and 514 are used. The polynomial model can
be implemented as follows:
R.sub.sfm(n,.lamda.)=aR.sub.sf(n,.lamda.).sup.2+bR.sub.sf(n,.lamda.)+c
(1)
where n and .lamda. are indices representing the target reflectance
values (2-99%) and the light wavelength, respectively, and a, b,
and c are wavelength-specific polynomial coefficients. The
polynomial coefficients allow a mapping of future measurements of
tissue spectra, R.sub.sf, to spectra, {tilde over (R)}.sub.sf, that
are normalized to the R.sub.sfm spectra. Equation 1 describes a
second order polynomial model. In general, however, polynomial
models of higher order (e.g., third order, fourth order, and even
higher order) can be implemented.
[0118] After normalization of the spectra recorded with light from
the two different illumination light ports 506 and 514, the spectra
recorded with the different illumination light locations are
orthogonalized (i.e., the spectral components due to the overlying
layers are removed from the spectra that include components that
are due to both the overlying and underlying layers.
Orthogonalization involves matrix multiplication. First, a
wavelength dependent weight, w, indicative of a correlation between
the spectra R.sub.sf and R.sub.sfm is determined from the following
equation:
w=R.sub.sfm.sup.T{tilde over (R)}.sub.sf({tilde over
(R)}.sub.sf.sup.T{tilde over (R)}.sub.sf).sup.-1 (2)
where the superscript T indicates the transpose of a matrix, and
{tilde over (R)}.sub.sf is a vector that corresponds to a spectrum
of reflected light recorded when light is incident on sample 102
from illumination port 514 and then photometrically mapped onto the
measurement space of R.sub.sfm. After the weight is determined, the
spectral features of the overlying layers can be removed by using
the following equation:
{circumflex over (R)}.sub.ort=R.sub.sfm-{tilde over
(R)}.sub.sfw.sup.T (3)
where {circumflex over (R)}.sub.ort is an orthogonalized spectrum
that results after the reflectance components due to skin and fat
are removed, and that substantially includes only information from
the underlying (muscle) layer 102m. {circumflex over (R)}.sub.ort
can be used with PLS or other multivariate calibration techniques
to develop calibration equations for determining chemical
properties of the underlying layer 102m from the orthogonalized
reflectance spectrum, {circumflex over (R)}.sub.ort, where the
calibration equations are independent of the optical effects of
overlying layers.
[0119] When the calibration equations are used in a medical
monitoring device, patient spectra can be collected with a fiber
optic probe of the same design as used to generate the calibration
equations, and the orthogonalized spectrum should be calculated
before it is used in the calibration equation.
2. Correction by Standard Normal Variate Scaling
[0120] Standard normal variate (SNV) scaling techniques can be used
to reduce scatter and other undesired contributions to measured
reflectance spectra. Suitable SNV implementations are disclosed,
for example, in R. J. Barnes et al., Applied Spectroscopy 43, 772
(1989), the entire contents of which are incorporated herein by
reference. SNV methods can be used, for example, to reduce
contributions to measured reflectance spectra that arise from
variations in optical properties of tissues of interest, such as
muscle tissues.
3. Correction by Principal Component Analysis Loading
[0121] In some embodiments, spectral reflectance measurements are
recorded from multiple locations on a subject's body, and/or from
multiple subjects. Variations in optical properties of tissues of
interest from one subject to another, for example, can introduce
variations into the reflectance data that are unrelated to
measurement analytes of interest. For example, in muscle tissue
reflectance spectra recorded from a set of subjects, a measurement
analyte of interest may be muscle tissue pH. However, in addition
to variations that arise due to changes in muscle tissue pH,
reflectance spectra recorded from muscle tissues in different
subjects can also include spectral contributions that arise from
variations in muscle tissue texture, and/or capillary density,
and/or fiber structure, and/or other structural properties of the
muscle tissues in the different subjects. These variations in
structural properties typically produce variations in
wavelength-dependent scattering coefficients for the muscle tissues
in the different subjects.
[0122] A large set of subjects may be used to model optical
property variations in tissues of interest. However, the time and
expense associated with measuring and analyzing reflectance spectra
from a large set of test subjects may make this approach
impractical for clinical applications. Alternatively, numerical
algorithms can be used to reduce and/or remove spectral
contributions in reflectance spectra that arise from optical
property variations in tissues of interest, prior to using the
reflectance spectral data in PLS modeling applications to measure
analytes of interest.
[0123] Principal component analysis (PCA) loading corrections can
be used to reduce and/or remove contributions to reflectance
spectra that arise from analyte-irrelevant variations in optical
properties of tissues of interest (e.g., tissues in which analytes
of interest are measured). Optical properties that exhibit such
variations can include scattering properties, absorption
properties, tissue refractive indices, and other properties. In
general, variations in infrared absorption by tissues of interest
are also related to concentrations of one or more analytes of
interest. Accurate measurement of analytes of interest may
therefore include determining and correcting for the
analyte-irrelevant contributions to reflectance spectra.
[0124] PCA analysis can be used to obtain spectral "signatures" of
the analyte-irrelevant variations, which can then be removed from
the spectral reflectance data via orthogonalization steps. PCA
loading corrections can be applied during both calibration and
predictive steps to further improve PLS models constructed from the
corrected spectral reflectance data.
[0125] Variations in spectral reflectance measurements that arise
from variations in optical properties of tissues of interest can be
reduced and/or removed in a series of steps. For example, in some
embodiments, a first analysis step includes determining variations
in spectral reflectance data that are not relevant to a target
analyte by PCA on a set of spectra collected from different
subjects (and/or from different locations on the same subject) in
the same calibration set with substantially similar values of the
analyte. The variations can be expressed as a set of loading
vectors of principal spectral components obtained from PCA. The
first analysis step is described by Equation 4:
X.sub.0,mc=X.sub.0-X.sub.0,mean=SP.sup.T+E (4)
In Equation 4, X.sub.0 is a matrix with dimensions m.sub.0.times.n.
Each of the m.sub.0 rows of X.sub.0 corresponds to a reflectance
spectrum recorded for a different sample used for PCA, and n is the
number of wavelength points in each reflectance spectrum. The
spectra in X.sub.0 include analyte-irrelevant spectral reflectance
variations. Matrix X.sub.0,mean has dimensions m.sub.0.times.n and
includes m.sub.0 rows, where each row is a 1.times.n vector whose
elements correspond to the column mean values of X.sub.0, so that
subtracting X.sub.0,mean from X.sub.0 yields matrix X.sub.0,mc with
dimensions m.sub.0.times.n, where X.sub.0,mc is a mean-centered
matrix of X.sub.0. S is a PCA score matrix with dimensions
m.sub.0.times.f.sub.0, where f.sub.0 is a number of principal
components used to model variations in X.sub.0. Matrix P is the PCA
loadings matrix and has dimensions n.times.f.sub.0. Matrix E, with
dimensions m.sub.0.times.n, is a matrix of spectral residuals of
X.sub.0 that are not modeled by PCA.
[0126] In a second analysis step, spectra used for PLS calibration
and spectra used for PLS-based prediction are orthogonalized with
respect to the loading vectors of the principal components obtained
in the first step. Spectral contributions due to variations in
optical properties of the tissues of interest are reduced and/or
removed in the corrected spectra which result from the second
analysis step. The second analysis step is described by Equation
5:
X.sub.ort=(X-X.sub.0,mean(m,n))-(X-X.sub.0,mean(m,n))P.sub.1P.sub.1.sup.-
T+X.sub.0,mean(m,n)=X-(X-X.sub.0,mean(m,n))P.sub.1P.sub.1.sup.T
(5)
In Equation 5, X.sub.ort is the orthogonalized (e.g., corrected)
spectral matrix with dimensions m.times.n, where m is the number of
samples, e.g., the m rows of X.sub.ort correspond to corrected
reflectance spectra recorded from m different samples. Matrix X
with dimensions m.times.n corresponds to m original, uncorrected
spectra. Matrix X.sub.0,mean(m,n) with dimensions m.times.n
includes m rows, where each row is a 1.times.n vector whose
elements correspond to the column mean values of X.sub.0. P.sub.1,
with dimensions n.times.f.sub.1, is a truncated loadings matrix,
where the number of columns f.sub.1 is equal to a number of
orthogonalization factors used in the orthogonalization procedure.
In general, f.sub.1 is less than or equal to f.sub.0, and a value
for f.sub.1 is selected on the basis of the element values in the S
and P matrices calculated in Equation 4. Following
orthogonalization, the corrected reflectance spectra in matrix
X.sub.ort can be used in PLS calibration and/or modeling to predict
values of analytes of interest.
4. Combined Correction Methods
[0127] In certain embodiments, any two or all three of the
short-distance methods, SNV methods, and PCA loading methods can be
combined to correct reflectance spectra by removing spectral
features that do not arise from measurement analytes of interest.
For example, in some embodiments, short-distance correction methods
can be applied first to a set of reflectance measurements to
correct for spectral features due to tissue layers that overlie
tissue layers of interest. SNV and PCA loading corrections can then
be applied to the short-distance-corrected spectra in succession to
correct for variations in optical properties in the tissue layers
of interest, e.g., where the set of reflectance measurements
includes reflectance data measured at different locations on a
subject's body, and/or reflectance data from different
subjects.
[0128] In general, the algorithms disclosed herein can be applied
in a desired order to correct spectral reflectance data. The
suitability of one or more correction algorithms applied in a
selected order to reflectance data is typically assessed by
determining the accuracy of a PLS model for an analyte of interest
that is developed based on the corrected spectral reflectance data
(discussed in more detail in the Examples).
Implementation
[0129] The equations and algorithms described above can be easily
implemented in hardware or in software, or in a combination of
both. The invention can be implemented in computer programs using
standard programming techniques following the method steps and
figures disclosed herein. The programs can be designed to execute
on programmable processors or computers, e.g., microcomputers, each
including at least one processor, at least one data storage system
(including volatile and non-volatile memory and/or storage
elements), at least one input device, such as a keyboard or push
button array, and at least one output device, such as a CRT, LCD,
or printer. Program code is applied to input data to perform the
functions described herein. The output information is applied to
one or more output devices such as a printer, or a CRT or other
monitor, or a web page on a computer monitor with access to a
website, e.g., for remote monitoring.
[0130] Each program used in the new system is preferably
implemented in a high level procedural or object oriented
programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine
language, if desired. In any case, the language can be a compiled
or interpreted language.
[0131] Each such computer program can be stored on a storage medium
or device (e.g., ROM or magnetic diskette) readable by a general or
special purpose programmable computer, for configuring and
operating the computer when the storage medium or device is read by
the computer to perform the procedures described herein. The system
can also be considered to be implemented as a computer-readable
storage medium, configured with a computer program, where the
storage medium so configured causes a processor in the computer to
operate in a specific and predefined manner to perform the
functions described herein.
[0132] Although any communications network can be used to obtain
results from remote monitoring, the Internet or wireless systems
provide useful choices to transmit data.
EXAMPLES
Spectral Effects of Short-Distance Corrections
Example 1
[0133] Experiments were performed to determine an optimum distance
(SD).sub.1 between the illumination probe 514 and detection probe
508 to correct for the presence of skin and fat over human muscle
in an embodiment of system 100. A probe 400 with an adjustable
short source-detector distance, (SD).sub.1, of 2 mm-6 mm and a
fixed long source-detector distance, (SD).sub.2, of 32.5 mm was
used to measure spectra from four different anatomical positions
(arm, calf, shoulder, and thigh) having different fat layer
thicknesses on four different subjects to determine which
(SD).sub.1 distance would result in the lowest variation in
measurements of an analyte for measurements performed on the
different locations of the person. Ideally, the spectral
differences caused by the different fat thicknesses from the
different positions on a person should be corrected after the
orthogonalization, i.e., all four spectra measured from different
body parts of a person should overlap, because the same information
should be recovered from a person's muscles regardless of what body
part is measured. Moreover, the spectral difference caused by the
skin color and different fat thickness on different people should
be decreased, i.e., a better correlation between the spectra and
the hematocrit ("Hct") values of patients should be obtained when
the spectra are orthogonalized to remove spectral contributions
from the overlying skin and fat layers.
[0134] Reflectance spectra were measured from the arm, calf,
shoulder and thigh of four human subjects using a fixed (SD).sub.2
distance of 32.5 mm and five different (SD).sub.1 distances of 1.83
mm, 2.5 mm, 3.0 mm, 4.0 mm, and 5.4 mm. The actual fat thicknesses
for the subjects were measured at different positions using
ultrasound and are listed in Table 1 below. The thigh position
chosen for this study was on top of the rectus femoris muscle
(front thigh), where thicker fat was found than the position on top
of the vastus lateralis muscle (side thigh). Each person's Hct
level was measured via invasive blood testing, and the levels are
listed in Table 2 below.
TABLE-US-00001 TABLE 1 Arm (mm) Calf (mm) Shoulder (mm) Thigh (mm)
Subject A 4.4 3.5 9.8 9.5 Subject B 1.8 5.8 9.0 10.7 Subject C 3.2
1.4 2.6 2.0 Subject D 6.3 9.5 11.6 18.6
TABLE-US-00002 TABLE 2 Subject A 41.5% Subject B 44.8% Subject C
43.4% Subject D 40.8%
[0135] FIGS. 9A-9E show the effects of short-distance corrections
for overlying tissue layers on the spectra recorded from a Subject
A for different (SD).sub.1 distances. The results in FIGS. 9A, 9B,
9C, 9D, and 9E were obtained for (SD).sub.1 distances of 1.83 mm,
2.5 mm, 3.0 mm, 4.0 mm, and 5.4 mm, respectively. In each figure,
spectra 810 are R.sub.sfm spectra obtained when different parts of
the subject's body were illuminated with light from illumination
port 506, spectra 820 are R.sub.sf spectra obtained when different
parts of the subject's body were illuminated with light from
illumination port 514, and spectra 830 are orthogonalized
spectra.
[0136] Differences among spectra recorded at different positions on
a subject's body appear generally as baseline shifts caused by the
different fat thicknesses, and are decreased by the
orthogonalization process. Corrected spectra 830 are closer
together than raw spectra 810. For probes having (SD).sub.1
distances of 1.83 mm, 2.5 mm, and 3.0 mm, the orthogonalized
spectra are closer together and their shapes are unchanged compared
to the raw spectra. For probes having (SD).sub.1 distances of 4.0
mm and 5.4 mm, the spectra are closer together after correction,
and certain features of the spectra are changed. For example, the
absorption peak due to hemoglobin (Hb) at 760 nm is less pronounced
after orthogonalization at (SD).sub.1 distances of 4.0 mm and 5.4
mm. This may imply that certain useful information from the muscle
layer (where significant Hb absorption occurs) is diminished by the
correction process. This is contrary to the objective of correcting
for the influence of fat and skin layers, but keeping the muscle
information. This effect may be due to deeper light penetration at
large (SD).sub.1 distances. As light penetrates more deeply inside
tissue, more information about the muscle layer is captured, and
the correction between the short (SD).sub.1 distance and the long
(SD).sub.2 distance may sacrifice some muscle information.
[0137] Similarly, FIGS. 10A-10E show correction results for spectra
from the four body parts of a Subject C for different (SD).sub.1
distances. While Subject A had Caucasian skin and Subject C had
Negroid skin, the results of the orthogonalization process are
similar for both subjects. Absolute absorbance values of Subject
A's and Subject C's skin are quite different, as evidenced by a
comparison of spectra 820 in FIGS. 9A-9E with the spectra 820 in
FIGS. 10A-10E. However, the corrected spectra for the two subjects,
for which the spectral components due to the skin are subtracted
out, are quite similar.
[0138] To examine correlations between corrected spectral
reflectance data and Hct values, mean absorbance values of the
spectra at the arm, calf, shoulder, and thigh of a subject were
calculated for each subject, and a relationship between the
subject's mean absorbance value and the corresponding subject Hct
value was established for each (SD).sub.1 distance before and after
short-distance corrections were applied to the reflectance spectra.
A predominant hemoglobin feature in tissue absorption spectra is
the deoxyhemoglobin peak at 760 nm, and the height of this peak in
the spectra for each subject should be linearly related to the
hematocrit level of the subject.
[0139] Table 3 shows the R.sup.2 correlation values of the
relationship between the mean absorbance at 760 nm for 4 different
anatomical positions and the Hct values for the four different
(SD).sub.i distances (i.e., 1.83 mm, 2.5 mm, 3.0 mm, and 4.0 mm).
From the values in Table 3, it can be seen that R.sup.2 improved
after the correction. This indicates that a strong correlation
between the spectra and the Hct values is established by correcting
the (SD).sub.2 distance spectra, R.sub.sfm, against the (SD).sub.1
distance spectra, R.sub.sf.
TABLE-US-00003 TABLE 3 (SD).sub.1 Distance Before After 1.83 mm
0.577 0.967 2.5 mm 0.480 0.996 3.0 mm 0.471 0.975 4.0 mm 0.476
0.925
[0140] From the above results, an (SD).sub.1 distance of about 2.5
mm may provide optimal results for this embodiment. In general, the
short source-detector distance can be determined through
experiments as described above, or through Monte Carlo modeling, if
the absorption and scattering coefficients of all the sample layers
are known.
Example 2
[0141] Another example of short-distance correction of reflectance
spectra to improve correlations between measured spectral
intensities and analytes of interest is shown in FIGS. 11 and 12.
FIG. 11 shows a series of measurements of maximum heme absorption
(including contributions from both hemoglobin and myoglobin) in 17
different human subjects. The spectral data from each subject was
collected from the flexor digitorum profundus. The spectral data in
FIG. 11 is uncorrected, and there is no strong correlation between
maximum heme absorbance and blood hematocrit levels measured for
each of the subjects.
[0142] FIG. 12 shows maximum heme absorbance versus blood
hematocrit calculated from reflectance spectra after short-distance
corrections were applied to the reflectance spectra to correct for
overlying skin and fat layers in the 17 subjects. A stronger linear
relationship is observed in the corrected data, suggesting that
predictive models such as PLS models based on the corrected
spectral data will more accurately estimate hematocrit levels in
patients and other subjects.
Spectral Effects of PCA Loading Corrections
Example 3
[0143] To evaluate the effects of PCA loading corrections on
measured spectral reflectance data, a set of reflectance spectra
were collected from human subjects performing a handgrip exercise,
and multi-subject venous blood pH models were developed and
evaluated based on the reflectance measurements. The handgrip
exercise protocol included a 2 second contraction of a subject's
hand, followed by a 1 second relaxation, at four different effort
levels: 15% maximum voluntary contraction (MVC), 30% MVC, 45% MVC,
and 60% MVC. As exercise intensity increases, pH falls to a lower
level at the end of each exercise bout. Each bout was 5 minutes in
length, and bouts were conducted 40 minutes apart. Blood was drawn
from a venous catheter placed in a vein close to the measurement
muscle. Samples were obtained just prior to each bout (to provide a
baseline pH measurement), every minute during the exercise bout,
and at 5, 10, and 20 minutes post-exercise. Blood samples were
measured using an I-Stat CG4+ cartridge (available from i-STAT,
East Windsor, N.J.) to determine venous pH. Spectral reflectance
measurements were made using an embodiment of spectrometer system
100 similar to the embodiment shown in FIG. 13.
[0144] Spectral reflectance data was measured from six different
subjects. Accuracy of PLS-based pH prediction models was estimated
before and after PCA loading correction of the spectral reflectance
data using a "leave-one-subject-out" cross validation procedure. In
this procedure, pH determinations for each subject are made based
on a calibration equation developed from data measured for the
other 5 subjects. Accuracy is estimated by calculating a root mean
square error of prediction (RMSEP) between pH values obtained from
NIRS reflectance measurements and from venous blood analysis.
[0145] FIG. 14 shows a set of calibration spectra from different
subjects at a single pH value (about 7.35) before PCA loading
corrections were applied. FIG. 15 shows the same set of calibration
spectra after PCA loading corrections were applied. Following
application of the correction procedure, the spectra at the same pH
are nearly coincident. This is consistent with a reduction of
subject-to-subject variations in the measured reflectance data
which are not related to the analyte of interest, e.g., muscle
pH.
[0146] Calibration equations were developed using PLS from
reflectance spectra and blood samples collected during the handgrip
exercise bouts. Blood pH values were interpolated at time intervals
corresponding to measured spectral data, where appropriate. FIG. 16
shows results of measured pH (for venous blood) versus predicted pH
(from NIRS measurements) for uncorrected reflectance spectra, with
calibration according to leave-one-subject-out cross-validation.
The diagonal line in the figure indicates a perfect match between
measured and predicted pH values. Predictions of pH based on
uncorrected spectra have an average correlation, measured by
coefficient of determination R.sup.2, of about 0.44, indicating
that a correspondence between measured and estimated pH values is
not strong.
[0147] FIG. 17 shows results of measured pH versus predicted pH
based on reflectance spectra that were corrected using PCA loading
correction methods. The R.sup.2 for the correspondence between
measured and predicted pH values based on the corrected spectra is
about 0.64 and the predicted pH values are more closely clustered
along the diagonal line, indicating that the pH model based on the
corrected spectra is significantly more accurate than the model of
FIG. 16. The RMSEP is 0.025 pH units, which describes the observed
scatter around the diagonal line.
Spectral Effects of Combined Correction Algorithms
Example 4
[0148] To evaluate the effects of combining multiple spectral
correction algorithms, three-layer (e.g., skin, fat, and muscle)
tissue-like solid phantoms were prepared. Agar (Agar A7049,
available from Sigma-Aldrich Inc., St. Louis, Mo.) was used as a
solid base material for the phantoms. Intralipid (available from
Baxter Healthcare Corp., Deerfield, Ill.) was used as a scattering
layer. Fabrication followed a procedure similar, for example, to
the procedure described in R. Cubeddu et al., Physics in Medicine
and Biology 42, 1971 (1997), the entire contents of which are
incorporated herein by reference, except that different absorbers
were used. Each layer was fabricated separately, and then skin and
fat layers were placed on top of the muscle layer.
[0149] Each phantom included a 1.0 mm thick skin layer with 0.15
mg/mL melanin (Melanin M8631, available from Sigma-Aldrich Inc.,
St. Louis, Mo.) as an absorber. Absorbers in the muscle layer were
a 2.2% solution of 6.times.10.sup.-4 mL/mL India ink (available
from Scientific Device Lab Inc., Des Plaines, Ill.) and NIR dye
ADS780WS (available from American Dye Source, Inc., Quebec, Canada)
of varied concentrations.
[0150] The concentration of the India ink was selected based on the
concentration of blood in tissue, which is approximately 2.2%. The
NIR dye has an absorption maximum at a wavelength of about 780 nm,
similar to deoxygenated hemoglobin, while India ink has relatively
wavelength-independent absorption properties. No absorber was
introduced into the fat layer since human fat typically has a very
low absorption coefficient. Reduced scattering coefficients
(.mu..sub.s') for skin and fat were 1.5 mm.sup.-1 and 1.2
mm.sup.-1, respectively. Tissue phantoms were prepared so that each
phantom had a muscle-reduced scattering coefficient (.mu..sub.s')
of 0.5 mm.sup.-1, 0.65 mm.sup.-1, or 0.8 mm.sup.-1, a fat thickness
of 2.0 mm, 4.0 mm, or 6.0 mm, and a muscle dye concentration of:
6.67 .mu.g/mL, 9.08 .mu.g/mL, 13.31 .mu.g/mL, 15.76 .mu.g/mL, 17.92
.mu.g/mL, 20.22 .mu.g/mL, 22.58 .mu.g/mL, 24.82 .mu.g/mL, or 26.68
.mu.g/mL. Sets of tissue phantoms having one of the foregoing
muscle dye concentration were fabricated so that each member of the
set had one of the 3 different levels of fat thickness and one of
the 3 different muscle scattering coefficients, e.g., nine
different phantoms were prepared with the same muscle dye
concentration. The phantoms were fabricated in three groups of 27
phantoms with low, medium or high muscle dye concentrations, over a
period of 3 days. Samples were sealed with plastic to avoid water
loss, covered with aluminum foil to avoid photo-bleaching, and
stored in a refrigerator to be measured in random order the day
after fabrication.
[0151] Reflectance spectra of each of the phantoms at 2.5 mm and 30
mm source-detector (SD) separations were measured using system 100.
An 8.5 W tungsten lamp (model 7106-003, available from Welch-Allyn
Corp., Skaneateles, N.Y.) was used for illumination of the
phantoms, and a spectrometer (USB2000, available from Ocean Optics
Inc., Dunedin, Fla.) was used to collect spectral reflectance data.
The two-distance probe of system 100 had one detector fiber bundle
and two source fiber bundles 30 mm (long distance) and 2.5 mm
(short distance) away from the detector bundle. A diameter of the
detector fiber bundle was 1.0 mm, and diameters of the source fiber
bundles at the long distance and at the short distance were 3.5 mm
and 1.0 mm, respectively. During collection of reflectance spectra,
the detector bundle was connected to the spectrometer, while the
two source fiber bundles were connected to the lamp via an on-axis,
or off-axis orientation. Computer controlled shutter system 110 was
placed in front of the lamp to switch between the two source fiber
positions so that only a single source fiber bundle was illuminated
by the lamp.
[0152] Reflectance spectra collected at the short distance (2.5 mm
SD) captured information from substantially only the skin and fat
layers of the phantoms. Spectra collected at the long distance (30
mm SD) included spectral information from the skin, fat, and muscle
layers.
[0153] Phantoms were separated into calibration and test sample
sets. Samples in the calibration set were chosen with properties
that were different from those in the calibration set to test PLS
model predictions on samples which may have been poorly and/or
incompletely modeled during calibration. Specifically, phantoms
with a fat thickness of 2.0 mm and various dye concentrations and
muscle scattering coefficients (a total of 27 phantoms) were used
as the test samples. The remaining samples (a total of 54 phantoms)
with fat thicknesses of 4.0 mm or 6.0 mm for each of the various
dye concentrations and muscle scattering coefficients were used for
calibration. None of the calibration samples had a fat layer with a
thickness of 2.0 mm, and none of the test samples had a fat layer
with a thickness of 4.0 mm or 6.0 mm.
[0154] A PLS model for dye concentration in the phantom muscle was
created using calibration sample data, and the model was validated
by predicting muscle dye concentrations from test sample spectra.
Leave-one-out cross-validation was used to determine the number of
PLS model factors. To evaluate the various spectral correction
methods, PLS regressions were performed with and without PCA
loading corrections and/or short-distance corrections and/or SNV
scaling corrections. Data within a spectral wavelength range from
700 nm to 900 nm was used in the analysis. Prediction accuracy of
the PLS models was described by R.sup.2 (the coefficient of
determination) between the estimated and actual dye concentrations,
and an estimated measurement error, which was calculated as a root
mean squared error of prediction (RMSEP) according to:
RMSEP=[(1/N).SIGMA..sub.i=1.sup.N(y.sub.i-y.sub.i).sup.2].sup.1/2
(6)
where N is a number of test samples, and y.sub.i and y.sub.i are
the estimated and actual dye concentrations. A large R.sup.2 value
and a low RMSEP indicate that the PLS model accurately predicts dye
concentration in the phantom samples.
[0155] Various spectral correction and data analysis algorithms
were implemented in programs written in version 7.0 of the
Matlab.TM. programming language (available from The Mathworks Inc.,
Natick, Mass.) and the version 3.5 of PLS_Toolbox.TM. (available
from Eigenvector Research Inc., Manson, Wash.).
[0156] In the following examples, where short-distance corrections
are applied to reflectivity spectra in combination with other
spectral correction methods, the short-distance corrections are
applied first. Where PCA loading corrections are applied to
reflectivity spectra in combination with other spectral correction
methods, the PCA loading corrections are applied last. For example,
where short-distance, SNV, and PCA loading corrections are applied
to spectral data, the short-distance corrections are applied first,
followed by the SNV corrections, and finally the PCA loading
corrections are applied.
[0157] Six calibration samples with the same dye concentration
(e.g., n=6) were used to obtain PCA loadings for PCA loading
correction of spectral data. The dye concentration at which the PCA
loadings were obtained will be referred to as the "calibration" dye
concentration subsequently. Loadings were obtained from PCA on sets
of six calibration samples, with each set of six samples having one
of nine different selected dye concentrations. Reflectance spectra
for each set of six samples were corrected using different
combinations of spectral correction methods to examine the effect
of the methods and their parameters on the calculated loading
vectors. To determine the number of loading vectors used in PCA
loading correction, leave-one-out cross-validation calibration
procedures were used in PLS models for dye concentration in the
phantom samples. Calibration was performed using 1, 2, 3, 4, and 5
PCA loading vectors at each of the selected dye concentrations. The
number of loading vectors and the selected dye concentration which
produced the smallest root mean squared error of cross-validation
(RMSECV) were selected for evaluation of the spectral correction
methods. Analysis of the phantoms determined that the first four
loading vectors obtained from PCA on the calibration spectra with a
dye concentration of 20.22 .mu.g/mL, provided the best prediction
results for the test samples. The three spectral correction
methods--short-distance overlayer correction, SNV correction, and
PCA loading correction--were compared individually and in
combination under the above conditions.
[0158] FIG. 18 shows an absorption spectrum of the ADS780WS dye in
aqueous solution. FIGS. 19 and 20 show phantom absorbance spectra
for short distance (e.g., 2.5 mm) and long distance (e.g., 30 mm)
illumination of the phantoms, respectively. FIG. 21 shows long
distance absorbance spectra corrected by applying short-distance
correction methods, as discussed previously. The short distance
absorbance spectra in FIG. 19 include reflected light from only the
skin and fat layers overlying the muscle layer in the phantoms. The
downward-sloping shapes of the absorbance curves are characteristic
of the absorber melanin in the skin layer. Further, baseline shifts
among the various curves are due to different fat layer thicknesses
among the various phantoms.
[0159] Long distance spectra in FIG. 20 have spectral shapes that
are similar to the dye solution spectrum shown in FIG. 18. The long
distance spectra include contributions from light reflected from
phantom muscle layers, where the dye is located. Variations in
absorbance, e.g., between 700 nm and 750 nm, are due to a
superposition of skin absorption and fat and muscle scattering
effects on the dye absorption. Short-distance corrections reduce
and/or remove skin absorption and fat scattering effects from the
spectral absorbance data. As shown in FIG. 21, the resulting
corrected spectra are more nearly coincident and resemble more
closely the spectrum shown in FIG. 18 than the uncorrected spectra
in FIG. 20. However, baseline variations that are attributed to
variations in optical properties of the muscle layer (e.g.,
variability in muscle layer scattering) between samples. PCA
loading correction algorithms can be used to reduce or remove
variations that arise from the non-uniformity of the optical
properties of the muscle layer among the phantoms.
[0160] FIGS. 22-27 show absorbance spectra of six calibration
samples, each with dye concentration 20.22 .mu.g/mL, but with
different combinations of fat layer thickness (4.0 mm or 6.0 mm)
and muscle scattering coefficient (0.5 mm.sup.-1, 0.65 mm.sup.-1,
or 0.8 mm.sup.-1). FIG. 22 shows uncorrected absorbance spectra. If
spectral effects due to fat and muscle scattering and skin
absorption were perfectly corrected among the various phantoms,
spectra corresponding to the same dye concentration would be
overlapped. In the spectra shown in FIG. 22, some baseline shift
and flattening is observed between 700 nm and 800 nm, and is
attributable to these scattering and absorption effects.
[0161] FIG. 23 shows spectra from FIG. 22 after short-distance
corrections for the overlying skin and fat layers have been applied
to the spectra. The corrected spectra are closer to one another as
a result of the applied correction, and have shapes that more
closely resemble the absorption band of FIG. 18. However, there are
still some differences among the corrected spectra, e.g., in the
spectral region near the absorption peak at about 780 nm. These
variations arise from muscle layer scattering, which is not
corrected by short-distance algorithms and methods.
[0162] FIG. 24 shows spectra from FIG. 22 after SNV scaling
corrections have been applied. The spectra are closer together
following application of the SNV methods, but a relatively flat
shape of the spectra indicates that skin and fat absorption and
scattering are not corrected by the SNV methods alone. In general,
SNV methods can be used to reduce and/or remove scattering
differences among multiple samples when variation among sample
spectra arises primarily from scattering, rather than analyte
variation.
[0163] FIG. 25 shows spectra from FIG. 22 after PCA loading
corrections have been applied to the spectra. Four loadings
obtained from PCA of calibration samples with dye concentrations of
20.22 .mu.g/mL were used to correct the absorbance spectra shown in
FIG. 22. The corrected spectra, shown in FIG. 25, are closer
together than the original uncorrected spectra. However, the
corrected spectra still show spectral features that are
attributable to melanin absorption in the skin layer of the
samples.
[0164] Combinations of the various correction methods can also be
applied to the absorbance spectra. FIG. 26 shows spectra from FIG.
22 after short-distance corrections and then SNV scaling
corrections were applied in succession. Compared with the
short-distance-corrected spectra of FIG. 23, the spectra in FIG. 26
are closer together and baseline variations are reduced, but are
still present.
[0165] FIG. 27 shows spectra from FIG. 22 after short-distance
corrections, SNV scaling corrections, and PCA loading corrections
were applied in succession. The spectra in FIG. 27 are highly
overlapped, and their shapes resemble relatively closely the dye
spectrum shown in FIG. 18 in the short wavelength region from 700
nm to 780 nm. Application of all three correction methods to the
spectral absorbance data significantly reduced spectral
contributions from skin and fat layer absorption and scattering,
and different muscle scattering coefficients arising from
analyte-irrelevant variations in optical properties of the muscle
layer.
[0166] PLS predictive models were constructed based on calibration
data and used to predict values of dye concentration in test
samples. Spectral absorbance data for the calibration and test
samples was either uncorrected, or corrected by applying
short-distance corrections only, SNV scaling corrections only, PCA
loading corrections only, a combination of short-distance and SNV
scaling corrections, a combination of short-distance and PCA
loading corrections, a combination of SNV scaling and PCA loading
corrections, or a combination of short-distance, SNV scaling, and
PCA loading corrections. A summary of model prediction results is
shown in Table 4. Each PLS model has large values of R.sup.2 (equal
to or greater than 0.95), indicating that a strong correlation
exists between predicted and measured dye concentrations. Without
any spectral processing, even though predicted and measured dye
concentrations were highly correlated, large prediction errors
resulted: the RMSEP was 4.31 .mu.g/mL, corresponding to an error of
21.54% of the total concentration range (from 26.68 .mu.g/mL to
6.67 .mu.g/mL).
[0167] With short-distance corrections only, SNV scaling
corrections only, or PCA loading corrections only, the RMSEP of the
PLS model decreased. Combinations of two different correction
methods further decreased the RMSEP of the PLS model, and combining
all three correction methods resulted in a PLS model with the
lowest RMSEP, 1.08 .mu.g/mL, a 5.3% percentage error, and a 3-fold
decrease in RMSEP relative to a PLS model where no correction
methods were used.
TABLE-US-00004 TABLE 4 Spectral Correction RMSEP Percentage Number
Methods R.sup.2 (.mu.g/mL) Error of Factors None 0.96 4.31 21.5 7
Short-Distance Only 0.97 3.86 19.3 7 SNV Scaling Only 0.97 3.16
15.8 2 PCA Loading Only 0.95 3.66 18.3 6 Short-Dist. + SNV 0.96
2.55 12.7 2 Short-Dist. + PCA 0.96 2.69 13.4 3 SNV + PCA 0.97 1.88
9.4 6 Short-Dist. + SNV + PCA 0.97 1.08 5.3 4
[0168] In certain embodiments, using spectral correction methods
can reduce a number of factors used in a PLS model. For example,
when PCA loading corrections were used along or in combination with
the other correction methods, the number of model factors was less
than the 7 factors used in a model based on uncorrected spectral
absorbance data. As an example, when the three correction methods
were used in combination, the PLS model for dye concentration in
the muscle layer of samples used 4 model factors. This reduction in
the number of factors for the concentration model was achieved
because correction of the spectral data removed variations due to
skin color, fat layer thickness, and muscle layer optical
properties that would otherwise have been modeled with PLS
regression.
[0169] Model prediction results are shown in FIG. 28 with no
correction of the spectra absorbance data, and in FIG. 29 with
correction of the spectral data using a combination of
short-distance, SNV scaling, and PCA loading corrections. The
diagonal line in each figures represents perfect prediction.
Comparing FIGS. 28 and 29, after correcting the spectra data using
three different correction methods, prediction results are more
closely clustered along the diagonal unity line, and the predictive
value of the PLS model is greater.
Example 5
[0170] The spectral correction methods disclosed herein have also
been applied to human tissue spectra. For example, where spectra
corresponding to different subjects, each having the same value of
a particular analyte of interest are available, PCA loading
corrections can be used to improve PLS models based on the spectra.
As an example, a range of pH values can be obtained for a subject
during periods of exercise. In general, muscle and blood pH
decreases during exercise as lactic acid is produced, and then pH
returns rapidly to baseline values when exercise is halted. If
absorbance spectra are recorded continuously during periods of
exercise and muscle and/or blood pH is simultaneously monitored, it
is possible to obtain spectral data at different times that
corresponds to the same pH value from different subjects performing
a similar exercise protocol.
[0171] To evaluate the PCA loading correction method on spectral
data from human subjects, a set of spectra and blood pH
measurements were collected from the forearms of three human
subjects performing a repetitive handgrip exercise protocol. Each
subject squeezed a test device for 4 seconds, then relaxed for 2
seconds, repeating this pattern for a total duration of 5 minutes.
Each subject performed four different exercise protocols at
different, successively increasing force levels, with a 30 minute
rest period between each protocol.
[0172] FIG. 30 shows a set of absorbance spectra collected from the
three human subjects at time points during the exercise protocols
where the blood pH of each subject was 7.37.+-.0.001.
Short-distance and SNV scaling corrections have been applied to the
set of spectral data. FIG. 31 shows the same set of absorbance
spectra shown in FIG. 30 after PCA loading corrections have been
further applied. The absorbance spectra in FIG. 31 have been
orthogonalized with the first four loading vectors obtained from
PCA of the spectra in FIG. 30. After PCA loading correction of the
spectral data, spectra from different subjects were significantly
more overlapped, indicating that spectral contributions due to
optical property variations in muscle tissues from one subject to
another, which were not correlated with pH, were significantly
reduced.
[0173] FIG. 32 shows all of the spectra from each of the three
human subjects during periods of exercise at all measured pH
values. Short-distance and SNV scaling corrections have been
applied to the spectral data. FIG. 33 shows the data from FIG. 32
after PCA loading corrections have been further applied. The data
was orthogonalized with the first four loading vectors obtained
from PCA of the calibration spectra in FIG. 30 that correspond to a
pH of 7.37.+-.0.001. Application of PCA loading corrections
significantly reduced variations in the spectra due to
subject-to-subject variations in muscle tissue optical properties.
Variations among the spectra shown in FIG. 33 correspond nominally
to absorbance variations resulting from exercise-induced changes in
muscle pH levels.
Other Embodiments
[0174] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
* * * * *