WO2001040776A1 - Method of measuring tissue hemoglobin saturation using gaussian decomposition - Google Patents

Abstract

The constituents of cerebral tissues that contribute to light absorbency, i.e., oxyhemoglobin, deoxyhemoglobin, water, lipid, cytochrome oxidase and a component for characterising light loss due to scattering, are further characterized and used to construct a model system that emulates cerebral tissue reflectance spectra in a variety of conditions. Using this model system in a reverse mode, compound spectra collected from brain tissue are decomposed into individual spectra features. The values for features attributable to oxyhemoglobin and deoxyhemoglobin are then used to construct a ratio that quantifies the percentage of total hemoglobin that contains oxygen. Because the major portion of light, collected by the detecting element of the equipment has transited through brain tissue, this ratio becomes a quantitative measure of brain tissue hemoglobin saturation. The decomposition analysis method is generally applicable to a variety of tissues besides brain tissue.

Description

METHOD OF MEASURING TISSUE HEMOGLOBIN SATURATION USING GAUSSIAN DECOMPOSITION

Field of the Invention

The present invention relates to determining hemoglobin saturation

in tissue, and, in particular, to a method of using gaussian decomposition

of diffuse reflectance spectra to determine hemoglobin saturation in

tissue.

Background of the Invention

The principal function of hemoglobin is to transport oxygen from

the lungs to body tissues. If the transport process is temporarily

interrupted at any step between the binding of oxygen to hemoglobin in

the lungs and its final conversion to water in the cells of the tissue, the

result will be a decrease in intracellular adenosine triphosphate, a

molecule used to store energy for cellular processes. If the interruption

continues over a prolonged time interval, the cells' inability to perform

routine cellular functions will eventually result in cell death.

Numerous pathologic mechanisms can reduce the amount of

oxygen in tissues (tissue hypoxia). Moreover, a number of surgical procedures, designed to repair or alter parts of a body's circulation

system, employ techniques that intentionally interrupt oxygen transport.

In the 1970s, Ohmeda introduced the pulse oximeter. This is an

inexpensive instrument that can be routinely applied to patients in a

variety of settings; at the patients bedside, in the operating room, and has

even become standard equipment on emergency vehicles. The device

allowed medically trained personnel, for the first time, to monitor a

patient's arterial hemoglobin saturation, without having to place an

arterial line. The instrument has achieved wide success and has become

entrenched as a standard for medical care. However, arterial hemoglobin

saturation is a parameter that conveys to the physician how well the heart

and lungs are functioning in their ability to keep blood oxygenated. It

does not provide any information about how well the tissues that use the

oxygen are performing, nor does it provide information about their status.

It simply provides the physician with a number that relates to the amount

of oxygen that is available to tissues, provided the tissues are functioning

normally and have available to them a means of drawing on this oxygen

pool.

In instances where blood pressure is low as a result of hemorrhage,

blood may be adequately oxygenated, but unavailable to tissues because

of a low perfusion pressure, or because the body has restricted the oxygen supply to that tissue in an attempt to conserve other organs, such as the

heart and brain. Another instance where arterial hemoglobin saturation is

of no value is in the case of endartarectomy. In this common medical

procedure designed to physically remove placque from the inner surfaces

of blood vessels supplying the brain, a surgeon may choose to apply a

clip across the affected side as he performs the procedure. He does this to

prevent blood loss and experience has shown that the clip can remain in

place for a finite period of time before it has to be removed to prevent

damage to the brain. The amount of oxygen that the brain on the operated

side receives during the procedure is dependent upon the degree to which

blood vessels cross-connected to the unoperated side can supply the

affected side. The amount of "collateralization" is dependent upon

numerous factors. Among these are the age of the patient, the size and

number of vessels, and the degree and stage of disease. Presently, the

surgeon relies upon indirect measures of stress to determine when and if

the brain's oxygen supply is compromised. These indirect measures are

other technologies that monitor aspects of function, such as evoked

potentials, or electroencephalographic activity. But, these measures

"report" deficiency only after function is compromised. Presently, there

are no available technologies in common clinical use that are capable of

forewarning the physician or surgeon that a state of oxygen deficiency is

either developing, or exists, in a specific organ's tissues. A number of 'laboratories are presently working to develop "tissue

oximeters". Early efforts focused upon oximeters directed at monitoring

the degree of saturation of hemoglobin in brain tissue using optical

methods that that can be performed noninvasively. Focus has been on

brain tissue, not only because of the importance of this organ, but also

because of the apparent advantages offered by the anatomy of the head.

The brain is relatively near the surface of the skin and is separated from

the environment by only a thin layer of optically transparent bone that

contains a minimal amount of blood. Measuring brain tissue hemoglobin

saturation has turned out to be a not so easy task to perform.

At least six different methodologies have been developed and used

to extract hemoglobin related information from brain tissue spectra. The

goal of each of these approaches is to quantify the two components of

hemoglobin individually, and in such a manner, as to allow the

components to be recombined in the form of a ratio that describes the

percentage of hemoglobin, contained within an optical field, that is

combined with oxygen. Generally, the methods that have emerged to

perform this task have done so from new insights or from new theoretical

prospectives that offer a potential solution to the tissue hemoglobin

saturation problem. Accordingly, instrumentation has been tailored to the

specific requirements of the analytical solution conceived. Simple, multi-wavelength, absorbencv-based algorithms

The distinguishing characteristic of approaches under this category

is that only optical attenuation is used in the computations required to

obtain a numeric solution. Typically, this information is input into an

algorithm in the form of optical density measurements monitored at two

to four select wavelengths. Historically, in vivo multi- wavelength

spectroscopy was first employed as a means of characterizing the in vivo

redox state of cytochrome oxidase. To do this, it was recognized that the

various forms of hemoglobin had to be characterized as well, because

each overlies the cytochrome oxidase spectrum. While it remains a goal

of most in vivo spectroscopists to develop a method that fully

characterizes cytochrome oxidase, most of the recent efforts have focused

upon ways to fully characterize hemoglobin concentration change. Multi-

wavelength absorbency based spectroscopy is attractive because it

requires simple and straight-forward mathematical computations.

Instrumentation design is relatively uncomplicated and economically

feasible. A weakness of multi-wavelength absorbency-based methods is

that they employ no means of characterizing light lost from the sampled

tissue by light scattering processes, and thus, in principle, can only report

changes in constituent concentration from an arbitrarily established

baseline. A number of instruments marketed today employ simple, multi-

wavelength, absorbency technology. These include products by the

Somanetics Corporation (INVOS 3100 and 4100), the Hamamatsu

Corporation (NIRO 500 and 1000), and NIM, Incorporated (Runman).

Two other instruments recently appeared in reports from the literature,

but there is little public information regarding the nature of their

technologies or methods for analytical solution. These are instruments

being developed by Hutchinson Technologies, Inc. of Hutchinson, MN

and by the Tostec Co., Inc. of Tokyo, Japan. All of these instruments

claim to be able to provide trend information that is based upon an initial

baseline setting and all claim to provide an accurate measure of tissue

hemoglobin saturation. Because the Somanetics INVOS 3100 instrument

was the first commercially available instrument in this country, it has

undergone the most evaluation. These studies have been disappointing in

some respects, for the clinician's confidence in this potentially useful

technology has waned in the aftermath. The INVOS 3100 instrument has

been revised and new reports on the INVOS 4100 are beginning to

appear.

Derivative spectroscopy

If it can be assumed that light scattering primarily influences

baseline absorbency, i.e., that light scattering is not strongly wavelength dependent, then baseline influences can be nulled by derivatizing spectra.

In 1989, it was shown that it is possible to compute a parameter, using

derivatized spectra, that correlates highly with brain tissue hemoglobin

saturation. While the method was demonstrated to be feasible, it was at

that time believed to be impractical for commercialization because the

algorithm was not portable to other instruments, i.e., the coefficients

employed were instrument specific.

Pseudo-random modulated code spectroscopy (PRM)

PRM technology was originally developed for use in satellite

technologies to measure the height of the ocean surface from space.

PRM spectroscopy evolved as a means of quantifying the much shorter

distances that photons travel in brain tissue. The PRM technique relies

upon a randomized sequence of light pulses of varying, but known,

widths. These pulse sequences are unique and when repetitively

transmitted into tissue and their emergence from the tissue is measured,

the sequences can be identified using autocorrelation analysis. By

comparing the input and output sequences, a time-shifted difference, or

time-of-travel for photons through tissue, can be characterized. The

scattering and absoφtion coefficients (μ_s and μ_a, respectively) can be

extracted from X² curve fitting the convolution of the instrument impulse response function with the theoretical reflectance function of μ_s and μ_a ,

respectively to the measured photon migration profile. Blood

oxygenation can be measured by performing the calculations at two

wavelengths and ratioing the absoφtion coefficients found there. The

advantages of the PRM technique is that it can be performed using

instrumentation that is relatively inexpensive to manufacture and safe to

use.

Time-resolved spectroscopy (TRS)

Because tissue is an effective multiple scatterer of light, the light

pathlength needed to perform quantitative calculations of hemoglobin

concentration is normally unknown. In 1988, it was shown that it was

possible to calculate a mean pathlength by measuring the time it takes

energy, pulsed in picosecond increments, to traverse the head. Time-

resolved spectroscopy has become a standard in the field of in vivo

spectroscopy because of its ability to measure pathlength directly.

However, it has not been demonstrated to be a method suitable for

clinical use for several reasons. TRS requires using very short laser

pulses and ultrafast photon counting equipment. Consequently, the

delivery of light and the recovery of light from tissues requires equipment

that is expensive, bulky, and requires a high degree of maintenance. Moreover, the intrinsic high peak power of picosecond lasers requires

further operational precautions.

Phase-modulation, or frequency-resolved spectroscopy (FRS)

FRS followed as a natural application of previous experiences with

radar and altimeter development. A coded light signal (typically

sinusiodal or square in shape) is injected into tissue. Photon diffusion

encodes the tissue characteristics in the timing of the delayed, received

pulse and in the intensity of the time profile. Thus, instead of receiving

a clean replicate of the transmitted pulse, the returned signals are spread

out over time and are diminished in amplitude. The signal information is

decoded using procedures similar to that described previously for the

PRM method. This technique, like that of PRM, requires relatively

inexpensive equipment to generate encoded light signals. It is safe to use

in that only small amounts of energy are required (microwatts). It

provides a measure of pathlength, thus allowing constituent concentration

to be computed directly from the Beer-Lambert relationship. The

disadvantage of the method is that it may be more susceptible to noise

than is PRM. Also, like PRM methods, FRS relies upon a mathematical

model of photon migration in a semi-infinite medium to recover tissue

absoφtion coefficients. Two instrument designs have been based on the FRS methodology.

The NIM instrument (PMD 4002, MM Incoφorated, Philadelphia, PA)

incoφorates three tissue-illuminating wavelengths, and a fourth,

reference wavelength. It is a hybridized design, thus, incoφorating both

FRS and multi- wavelength, absorbance-based methodologies. It has

been tested, both in vitro, and in vivo, in animal experiments. Tissue

hemoglobin saturation measurement made with this instrument system

show an excellent linear correlation with tissue hemoglobin saturation

estimated from measurements made on whole blood. Another instrument

is presently marketed by ISS, Inc. This instrument has not undergone

rigorous evaluation. It is a two-wavelength, four-channel device.

Photoacoustic spectroscopy

When light energy is absorbed by molecules of a tissue, heat is

liberated in the process. This quickly results in thermal expansion of the

surrounding tissue, which, in turn, is followed by a rapid collapse to the

original state. The result is an acoustic signal that can be "heard" in the

ultrasound range. The amplitude of the acoustic signal is proportional to

the amount of light absorbed. Most importantly, the amplitude of the

signal is related to the total amount of light absorbed, including that

which is contained in the photon migration path. It has been

demonstrated that this method is feasible when applied to biologic tissue. However, while the method is advantageous in being insensitive to

changes in the light scattering properties of tissue, it was later found that

the useable signal originates close to the surface of the tissue, and was,

thus, not suited for measuring changes in tissue constituent composition

in deep structures.

At least six different approaches have been used to extract

constituent information from spectra collected from tissues. All of the

techniques described use procedures that are noninvasive and non-

damaging to the tissue. With the exception of TRS, most of these

methods can be performed using equipment that is relatively inexpensive

to construct and maintain. TRS, FRS, and PRM technologies are at the

forefront of development because they incoφorate solutions for

measuring pathlength, a necessary component of the Beer-Lambert

solution for absolute constituent concentration. Multi-wavelength

methodologies have become established for trending measurements, but

they cannot be used for quantitative measurement. All methods are

susceptible to noise because of the low light level requirements

established for patient safety. Excepting derivative spectroscopy, all

methods are multi-wavelength and use absorbency changes occurring at

only two to four points within the entire near infrared ("NIR") range.

Hence, all methods, to a greater or lesser extent, are subject to how well absorbency changes at these wavelengths reflect changes occurring

within the monitored tissue.

Summary of the Invention

An object of the present invention is to provide a method of

measuring tissue oxygenation that can be performed using

instrumentation that is portable and inexpensive to make, such that

bedside and field measurements of this parameter can be quickly and

accurately made.

The present invention is a method for decomposing compound

diffuse reflectance spectra, collected from tissue, such as brain tissue, into

discrete constituents that can then be used to compute tissue hemoglobin

saturation. The present invention requires a spectrometer capable of

collecting full absoφtion spectra in the NIR band and a computer for data

acquisition and processing. Because constituents other than hemoglobin

must be quantified in the process of extracting this information, the

method additionally yields qualitative information about these

constituents. In the case of brain tissue, these secondary parameters are

related to the water content of the brain tissue, the redox state of brain

tissue cytochrome oxidase, cerebral tissue lipid content, and the brain's

light scattering properties. The secondary constituent characterizations are qualitative, but the values can be used for trending puφoses, or for

comparing relative amounts between tissue regions or between brain

hemispheres, when collected and analyzed as a function of optical path

length. The method of the present invention can be further developed to

quantify all of these secondary parameters.

The constituents of cerebral tissues that contribute to light

absorbency, i.e., oxyhemoglobin, deoxyhemoglobin, water, lipid,

cytochrome oxidase and a component for characterizing light loss due to

scattering, are further characterized and used to construct a model system

that emulates cerebral tissue reflectance spectra in a variety of conditions.

Using this model system in a reverse mode, compound spectra collected

from brain tissue are decomposed into individual spectral features. The

values for features attributable to oxyhemoglobin and deoxyhemoglobin

are then used to construct a ratio that quantifies the percentage of total

hemoglobin that contains oxygen. Because the major portion of light

collected by the detecting element of the equipment has transited through

brain tissue, this ratio becomes a quantitative measure of brain tissue

hemoglobin saturation. Because it is a ratio, it does not require a

knowledge of in vivo molar absoφtion coefficients and it is relatively

pathlength insensitive . BRIEF DESCRIPTION OF THE DRAWINGS

FIGURE 1 is a graph showing the absoφtion spectra of

oxygenated and deoxygenated hemoglobin.

FIGURE 2 is a block diagram of the instrumentation used to carry

out the method of the present invention.

FIGURE 3 is a graph showing NIR prediction versus measured

tissue hemoglobin saturation.

FIGURE 4 is a graph showing that the oxyhemoglobin feature

increases during hypoxic-hypoxia.

FIGURE 5 is a graph showing that adding linkage between the 760

nm and 930 nm features corrects the directional error in recovered

oxyhemoglobin

FIGURE 6 is a graph showing the ratio of oxyhemoglobin

absoφtion to absoφtion attributable to total hemoglobin is proportional

to tissue hemoglobin saturation.

FIGURE 7 is a graph showing that the ratio of oxyhemoglobin

absoφtion to absoφtion attributable to total hemoglobin can be adjusted using two coefficients to provide a numerical predictor of brain tissue

hemoglobin saturation.

FIGURE 8 is a graph showing the best and worst case fits to

absoφtion spectra and the error of prediction.

FIGURE 9 is a graph showing that feature absorbency is optrode

separation distance dependent.

FIGURE 10 is a graph showing tissue hemoglobin saturation,

measured by gaussian decomposition methods is optode separation

distance independent.

FIGURE 11 is a graph showing water corrected spectra from bacon

and white matter.

FIGURE 12 is a graph showing the result of fitting the HbSat_bt

ratio to computed tissue hemoglobin saturation.

FIGURES 13 A and B are graphs showing the effect linking two

deoxyhemoglobin features has on the total hemoglobin attenuation.

FIGURE 14 is a graph showing an absoφtion spectrum from cat

head and the goodness-of-fit provided by Model C. FIGURE 15 is a graph showing an absoφtion spectrum from

gastrocnemius and the goodness-of-fit provided by Model C.

DESCRIPTION OF THE PREFERRED EMBODIMENT

An absoφtion spectrum is a plot of how light within a range of

energies is absorbed by molecules. For pure substances (constituents) in

a dilute solution, the amount of light absorbed at any particular energy

level is proportional to the concentration of the constituent in the solution.

The spectrum for a particular constituent can be thought of as being

analogous to a fingeφrint, with it's shape being it's most distinguishing

characteristic. It may be simple or complex, depending on the number of

absorbers present, but it is uniquely shaped. For mixtures of constituents

in a solution, a compound spectrum is observed. This compound

spectrum results from the summation of all absorbencies by all of the

substances contained within the mixture. Compound spectra can be

broken down into basic elements if the features of all of the possible

constituents are known.

The Beer-Lambert law states that absoφtion, A, at a selected

wavelength, λ, is proportional to the concentration of a constituent, c, and the pathlength, L, through which the light travels during measurement, so

that:

A(λ) = ε(λ)cL (1)

where ε(λ) is the molar absoφtion coefficient and is wavelength

dependent. For many absorbers, ε(λ) has the characteristics of a gaussian

probability distribution and is the property that lends shape to the

absoφtion spectrum. For simple constituents (constituents with a single

absorbing center) having these properties, an equation of the form

[Aj = ε_mxi c L EXP((- .5((λ-λ_c)/FWHM) ²) (2)

may be written, where [A]χ_min..λmaχ s an array, or range of absorbencies,

or an absoφtion spectrum, ε_max is the maximum value of the absoφtion

spectrum within the range, FWHM is the width of the spectrum at

l/2*ε_max, and λ_c is the wavelength around which the spectral range is

centered. Complex spectra, i.e. constituents with multiple absorbing

centers, can be described as a summed representation of equation 2,

wherein each center is described. Equation 2, or its summed

representation, thus describes constituent shape; a property that is unique

for every substance. Note that when λ_c equals λ, the terms inside the

brackets reduce to zero and the exponentiated portion of the equation takes on the value of unity. In this special case, equation 2 reduces to

equation 1. It follows that c may be determined from [A]_λmi_n..χ_max, if ε_max,

L, FWHM, and λ_c are known.

Decomposition of compound spectra requires an a priori

knowledge of the absoφtion properties of all constituents that absorb

within the specified region of optical monitoring. This information may

be termed a spectral feature database. Because some compounds have

similar features at similar energy locations, rules regarding how features

interact must also be known before they can be separated properly. For

example, the deoxyhemoglobin molecule has two absorbencies in the

700-1100 nm NIR band, one of which overlaps with oxyhemoglobin. To

separate a compound spectrum containing oxyhemoglobin and

deoxyhemoglobin into elemental spectra, a rule is needed indicating that

the deoxyhemoglobin contribution to the region overlapping

oxyhemoglobin will be a constant fraction of the non-overlapping feature.

One other factor must be known. Because photons shone into tissues are

also subject to light scattering, the knowledge base must also include

rules to account for "apparent" absoφtion, or light loss. The spectra

feature database, used in conjunction with a rule set and a definition

regarding how light scattering contributes to baseline absoφtion, is herein

defined as a knowledge base or, in engineering terminology, a model system. It follows that, if the model system completely describes the real

system, then it can be applied to real spectra in a reverse manner and used

to derive the relative concentrations of the constituents that contribute to

a compound spectrum. A least squares curve fitting procedure is used to

adjust the parameters of a model system until the error between the

experimental spectrum and the model spectrum is minimized. The

individual contributing constituent information can then be recovered.

The constituents of cerebral tissues that contribute to light

absorbency are known. According to the present invention, these

constituents are further characterized and are used to construct a

constrained parameter, model system that emulates cerebral tissue

reflectance spectra in a variety of conditions. The knowledge base

incoφorates information about oxyhemoglobin, deoxyhemoglobin, water,

lipid, cytochrome oxidase and includes a component to characterize light

loss due to scattering. Using this model system in a reverse mode,

compound spectra collected from brain tissue are decomposed into

individual spectral features. The values for features attributable to

oxyhemoglobin and deoxyhemoglobin are "recovered" and used to

construct a ratio that quantifies the percentage of total hemoglobin that

contains oxygen. Because the major portion of light has transited through

brain tissue, this ratio becomes a quantitative measure of brain tissue hemoglobin saturation. This has been experimentally verified. The

model system was built from spectral information collected in dogs and

cats, but is generally applicable to other mammalian species, including

man.

One hemoglobin molecule consists of two alpha and two beta heme

containing polypeptide chains (globins). Together, the chains form a

single molecule with four heme units that is capable of carrying four

oxygen molecules. The features present in a sample spectrum of

hemoglobin depend on the relative proportion of the number of

hemoglobin molecules that contain bound oxygen. In the fully

oxygenated form, features of high absorbency are located at 415, 542,

577, and 930 nm. In the deoxygenated form, the features shift location

and are located at 431, 555, 760, and 910 nm. Prior work in monitoring

brain tissue hemoglobin saturation has focused on identifying the

constituents in brain that have absorbing properties in a narrow energy

band known as the near-infrared window ("NIR"), i.e., 700 nm to 1100

nm. This is because this energy window contains only weak absorbencies

which allows light to penetrate more deeply into tissue than in other parts

of the light energy band. The features present in the NIR band, for

oxygenated and deoxygenated hemoglobin, are illustrated in Figure 1.

Above the upper energy cutoff of the NIR window, i.e., λ < 700 nm, a strong absorbency by the visible hemoglobin features prevents light from

penetrating deeply. Below the lower energy limit, i.e., λ > 1100 nm, light

is absorbed superficially by a strong water absorbency. At least

identifiable features are contained within the NIR window of biological

tissues. Three features are attributed to hemoglobin, two to lipid, two to

cytochrome oxidase, and three to water. Of the four primary NIR

absorbers, water, hemoglobin, cytochrome oxidase, and lipid, water is the

most abundant absorber (82% by weight), followed by lipid (-12%),

hemoglobin (-0.4%), and cytochrome oxidase (<0.1%).

FIGURE 1 shows the absoφtion spectra 11 and 12 of oxygenated

and deoxygenated hemoglobin, respectively. The spectra 11 and 12 were

collected using a fourier transform spectrometer (Model 2000, Perkin-

Elmer Coφoration). The spectra 11 and 12 were collected in

transmittance mode while illuminating a sample of whole blood perfusing

through a 0.1 cm pathlength quartz flow-through perfusion cell that had

been inserted into an arterial-venous shunt, created in a cat animal model.

Hemoglobin saturation during the period when the oxygenated spectrum

was collected, measured 98.7% on a benchtop blood gas analyzer (ABL,

Radiometer). During collection of the deoxygenated spectrum 12, it

measured 25.3%. Both spectra have been post-processed and corrected

for instrument tilt and water absoφtion. The absorbing properties of hemoglobin have been extensively

studied. In normal individuals, the predominant hemoglobin forms are

oxyhemoglobin and deoxyhemoglobin, although other forms of the

molecule may rise to significant concentrations in unhealthy individuals.

In the fully oxygenated state, oxyhemoglobin absorbs light in a broad

portion of the NIR band and has a peak absorbency centered at

approximately 930 nm. As oxyhemoglobin deoxygenates, absorbency at

930 nm decreases in magnitude and appears to shift to a new center

wavelength at 910 nm. Simultaneously, absorbency at all wavelengths

below approximately 815 nm increases and a new feature is seen to form

at 760 nm. It is commonly assumed that the changes in peak magnitude

at 930 nm and at 760 nm can be proportionately graded between the fully

oxygenated and fully deoxygenated states. Moreover, when the NIR

band features are used, it is common practice to ignore the visible and

ultraviolet band features.

The spectral characteristics of cerebral lipids have not been

published. Prior work shows that animal fat absorbs significantly at 928

nm and -1030 nm.

Table 1 itemizes the major NIR band absorbing constituents of

cerebral tissue and their center wavelength properties.

Table 1. Constituents of brain spectra published in the current literature.

Using data obtained from the literature, the individually

identifiable constituents of the NIR band have been characterized in a

manner that allows a knowledge base to be constructed. The knowledge base consists of a series of equations that characterize each of the

absorbing features by their center wavelength, λ_c, and full width at half

maximum ("FWHM"). The shape of an absorbing feature is assumed to

have gaussian properties and is described as,

[OD]_{λmn λmax} = h_cEX?(-0.5((λ-λ_c)/FWHM) ²) (3)

where: [OOJ_λmm χ_maκ is a range of optical density, or absorbence, and is

dimensionless; λ is wavelength, in nanometers; FWHM is the full width

at half-maximum, in nanometers; and h_cis peak height; and λ_c it the peak

center wavelength, in nanometers. Note that here h_c is a composite

parameter equal to ε_mΑxcL identified in equation 2, and is, therefore,

without dimensions. Thus, a compound tissue spectrum becomes the

summation of the simple constituent features superimposed on an

apparent baseline absoφtion attributed to light loss due to photon

scattering. Equation 3 is replicated n times, for the n absorbency peaks

occurring in the NIR window and a constant, k, is added. Thus, for a

compound or complex spectrum, shape is defined as

n

[OD]_{λτmn λmax} = ∑ (OD_λ)_n + k (4)

which describes the general case knowledge base. Baseline OD is

assumed to be wavelength independent. To account for features outside

of the 700-1100 nm band that overlap into it and contribute absoφtion, it was assumed that features at 555 nm and 577 nm "splay" into the NIR

band as a result of the high absoφtive properties. A first model system,

termed herein as Model A for descriptive puφoses, was then devised on

the basis of the features shown in Table 2.

Table 2. Features of the first model system used to extract tissue hemoglobin saturation from in vivo collected head spectra. Animal Studies and Surgical Procedures

All experiments described herein were pre-approved by the Animal

Care and Use Committee of the Johns Hopkins University Medical

Center. In 21 dogs, anesthesia was induced using a combination of

sodium pentobarbital and fentanyl anesthesias, administered

intravenously. Anesthesia was maintained using only fentanyl. Cannula

were placed in the femoral arteries and veins to enable measurements of

arterial blood pressure and to collect arterial blood samples. A small

sized cannula was placed in the superior saggital sinus to collect samples

of cerebral venous blood. All blood gases were measured using a

standard laboratory blood gas analyzer (ABL, Radiometer Coφoration,

Copenhagen, DK). Hemoglobin saturation and content were measured on

an OSM3 (Radiometer coφoration, Copenhagen, DK). Brain tissue

spectra were collected through exposed skull after the temporalis muscle

was removed, by placing two fiber optic bundles directly on the skull

surface. The "optodes", that is, the fiber bundles delivering and

collecting light at the skull's surface, were sealed using light opaque black

modeling clay.

Several protocols for data collection were used, depending upon

the type of information sought. Hvpoxic-hypoxia: All animals were challenged with multiple

levels of reduced inspired oxygen concentration. The order of

administering the hypoxic gas mixtures was usually from normoxic levels

to severely hypoxic levels, usually in five to eight decremental steps. At

each level, steady-state conditions were a requirement before collecting

paired arterial and cerebral venous blood samples for analysis. A sample

spectrum was collected either before or after the blood samples.

Hypercapnia: In twelve of these animals, C02 was separately

administered to induce tissue hyperoxygenation.

Anemic-hypoxia: In another two of the animals, anemic-hypoxia

was induced by infusing two liters of lactated ringers while exchanging

equivalent volumes of blood.

Hypothermia: In one animal, body temperature was cooled to 18

°C. Blood samples and spectra were collected during the period of body

temperature reduction and after cooling was achieved graded hypoxia

was induced stepwise to cover a large range of arterial blood hemoglobin

saturations.

Figure 2 is a schematic illustration of the instrumentation 10 used

with the above studies, and which could be used with the present invention. Light is generated via a high intensity halogen lamp 13

powered by a power supply 14. The light 15 from lamp 13 is shined

through a lens 16 into a Perkin Elmer FTIR 2000, Fourier transform

spectrometer 18 (Model 2000, Perkin-Elmer Coφ, Norwalk, CT) that was

specially modified for this application by the instrument division of

Perkin Elmer. The modifications allowed for the use of external light

source 13 (Model 66183 halogen external light source; Oriel Instruments,

Stratford, CT) and for the instrument to pass it's light output 15 to the

input 20 of a 6-foot, 5 mm diameter, steel shielded fiber optic bundle 22,

also known as an optode. The output end 24 of bundle 22 was placed in

contact with the exposed skull 29 of an animal. Light 15 passing through

the bundle was deposited on the skulls surface 32, whereby some photons

migrated through the tissues of the skull, dura, cerebrospinal fluid, and

brain 30, to exit at a distant site on the skulls' surface 28. A portion of the

exiting light 26 was collected by a short, 3 -foot length of shielded fiber

optic bundle 34, also an optode, and passed to a peltier-cooled avalanche

photodiode 36 ("APD"); (Advanced Photonix, Inc., Camarillo, CA).

APD 36 is powered by a high voltage power supply 38. The signal from

APD 36 was then passed to the control of a computer 40 after being

filtered digitally by filter 42 (Model SR650, Dual channel,

Highpass/lowpass Programmable Filter, Stanford Research Systems, Inc.,

Sunnyvale, CA). The data acquisition and timing schemes for light generation and collection were software controlled. Additional software

options allowed for control of the number of spectra collected, the

number of scans averaged, and output aperture control using neutral

density filtering. Post-hoc spectral processing was performed using

Spectrum (Perkin-Elmer Corporation, Norwalk, CT) when necessary.

Data analysis and spectral decomposition

Decomposition analysis routines were written using two software

packages. Manual curvefitting was performed using Sigmaplot for

Windows, version 5.01 (Jandel Scientific, San Rafael, CA). Automated

processing of bulk spectra was performed using a routine written for

MatLab, version 4.0, (The Mathworks, Inc., Natick, MA). Using these

software routines, Model A was applied to resolve h_c for each of the ten

spectral features and baseline OD (k).

One of the goals of the study was to determine if a non-invasive

measurement of tissue hemoglobin saturation could be computed from

absoφtion spectra obtained from an animal's head. It must be kept in

mind that the term "tissue hemoglobin saturation" is a construct that

refers to the average saturation of hemoglobin of brain tissue expressed

as a percentage of the total hemoglobin. In actuality there is no standard

method for determining the value of this parameter. The term "tissue hemoglobin saturation" stems from a literature citing that suggests

approximately 75% of the blood volume of tissue is contained within the

venous system. On this basis, it has been assumed by a number of

laboratories that approximately 25% of the hemoglobin within a tissue is

contained within the arterial compartment of the vasculature and that 75%

is contained in the venous compartment. Thus, one may estimate a

theoretical "standard" for comparative puφoses. Realistically, the

proportioning between compartments is probably not constant or uniform.

For the study, a distribution of 10%:90% was arbitrarily selected, rather

than the traditional 25%:75%. This was done because the final

relationships between Hb02 / THb and the standard were nonlinear.

Choosing a 10%: 90% ratio appeared to linearize the relationship.

Herein the term "Standard HbSat_bt" refers to a computed value that

is believed to represent the average percentage saturation of hemoglobin

contained within a field that is defined by the migration path of photons

shined into tissue. Standard HbSat_bt, computed as 0.1 * HbSat_an + 0.9 *

HbSatcven, where HbSatgr_t and HbSat_cven are the percentage saturation of

hemoglobin in arterial and cerebral venous blood, respectively. Standard

HbSat_bt was used to verify the NIR HbSat_bt method. The features were

resolved independently using a nonlinear curve-fitting algorithm

constrained to yield only positive values for the peak height. Figure 3 is a graph showing NIR prediction versus measured tissue hemoglobin

saturation. Figure 3 shows that the ratio of optical attenuation at 920 nm

to the sum of optical attenuations of 920 and 760 nm is linearly correlated

to tissue hemoglobin saturation estimated as a weighted fraction of

arterial and cerebral venous hemoglobin saturation. This figure also

shows the results of fitting 180 absoφtion spectra, collected in 21 dogs,

to constituent Model A. Thirty-seven spectra were collected in

normoxic-normocapnic conditions (NN; Pa_C02 =31-42 mmHg); 98 during

conditions in which hypoxis-hypoxia was induced (HH; Pa_o2 8 - 100

mmHg); 26 during hypercapnia (HC; Pa_C02 > 40 mmHg; 4 during anoxia

(AX; Pa_02< 8mmHg; 8 in conditions where vascular hemoglobin

concentration was varied (AH; (Hb)a 9-20 g/dl); and 7 during

hypothermia (LT; rectal temperature <30°C). The correlation observed in

each sorted condition was not different from the correlation established

using pooled data. Figure 3 further shows that the ratio 50 tHb0₂/THb is

proportional to HbSat_bt, over the entire standard HbSat_bt range 52.

However, Figure 4 shows when the constituents were split out and plotted

against the standard 48, it was found that both oxyhemoglobin 46 and

deoxyhemoglobin 47 were inversely proportional to tissue hemoglobin

saturation 48. Although this is theoretically possible in special

conditions, (e.g., when the fractional extraction of oxygen across the brain decreases), the measured extraction fraction indicates such a change

could not occur. It was concluded that the oxyhemoglobin measure,

computed using Model A, yielded an erroneous result. It was reasoned

that such an error could arise from cross-talk between oxyhemoglobin

and another constituent. The most likely candidate for cross-talk is the

secondary deoxyhemoglobin peak that overlaps the oxyhemoglobin

spectrum. In Model A, h_c for the secondary peak was resolved as though

the variable was independent of all other variables. In fact, it's value is

related to that of the 760 nm variable, because the two peaks are part of

the same complex spectrum.

The model system was revised to include two rules. First, it was

assumed that the deoxyhemoglobin feature at 907 nm was not

independent of the feature at 760 nm, but rather changed proportionately

with the 760 nm feature. Secondly, the location of the feature was

refined. In a small group of test data, the location was determined to be

937 nm, rather than the previously published 907 nm. Hence, a rule,

h937nm_> = ct h_760nm, was added to the model to account for the linkage,

where is a proportionality constant taken as the ratio of two specific

absoφtion coefficients at the two wavelengths.

A second change was made in the model system to account for the

shoulder region discussed previously when it was found that the 555 nm and 577 nm features are better characterized as lorentzian shapes, than as

gaussian shapes. As such, these features probably do not contribute

significantly to the NIR band. However, the features of the ultraviolet

band appear to be of such magnitude that they may still be recognizable

in and contribute tilt to, NIR band spectra. Thus, it was decided to

emulate the ultraviolet features using a singular gaussian-shape having a

center location of 391 nm and a FWHM of 150 nm. Although 391 nm is

well above the known location of the ultraviolet features, this value

provided the best fit in a test dataset when the center wavelength of a

single gaussian-shaped peak was assumed and it's FWHM resolved as an

independent variable.

The revised model system, Model B, was reapplied to the

previously-collected spectral library. The results are shown in Figures 5,

6, and 7. Figure 5 shows recovered optical attenuations due to different

hemoglobin constituents - pooled results from fitting to Model B. To

prevent cross-talk between overlapping oxy- and deoxyhemoglobin peaks

in the 920-940 nm region, the deoxyhemoglobin peak at 937 nm was

linked to the deoxyhemoglobin peak at 760 nm. In this fitting result, the

linkage forces a solution where attenuation of 937 nm is 0.6 times

attenuation at 760 nm. The effect on the recovered oxyhemoglobin

attenuation 60 is such that a reduction in this parameter value is observed as tissue hemoglobin saturation is reduced. In contrast, deoxyhemoglobin

62 rises.

Figure 6 shows the ratio 64 formed as attenuation due to

oxyhemoglobin to the summed attenuations due to oxy- and

deoxyhemoglobin is a linear function 66 of standard tissue hemoglobin

68. The effect of linking the primary and secondary deoxyhemoglobin

peaks and forcing a common solution, is to steepen the slope of the

correlation.

Figure 7 shows the Model B prediction of HbSat_bt- Tissue

hemoglobin saturation 70 can be predicted from recovered optical

densities at 920 and 760 nm (see Eq 5). A prediction equation of the

form

OD₉20nm - C₁(OD₉20nm + OD 60nm)

NIRSatbt = (5)

can be written. The coefficients C] and c₂ are the intercept and slope,

respectively, of the tHb02/THb ratio to the standard tissue hemoglobin

saturation plot. The values of c and c₂' used to adjust the correlationship

to the line of identity, were 0.4284 and 0.0046, respectively, for this

dataset. In a population of 21 animals, the standard error of prediction if

6.5%. Prediction error is least at tissue saturations greater than 30%.

Below this level the error of prediction is increased, but it is presently unclear as to whether the fault lies with the model, or with the estimate

used as the standard of comparison.

Referring again to Figure 5, Model B yielded relationships for

deoxyhemoglobin and oxyhemoglobin that were directionally correct,

i.e., when tissue hemoglobin saturation 58 is high, tissue oxyhemoglobin

60 is high and tissue deoxyhemoglobin 62 is low, and when tissue

hemoglobin saturation 58 is low, tissue oxyhemoglobin 60 is low and

tissue deoxyhemoglobin 62 is high. The sum of the two hemoglobin

parameters was inversely related to tissue hemoglobin saturation during

hypoxic conditions and directly proportional to tissue hemoglobin

saturation during hypercarbia (data not shown). This is consistent with

hypoxia induced vasodilation and hypercarbia induced vasodilation;

given that both are initiated from the normoxic-normocarbic state. As

was observed using Model A, Model B yielded an overall excellent linear

relationship between the ratio of the 920 nm constituent to the sum of the

920 nm and the 760 nm constituents (tHb02/THb).

Figures 8A and B illustrate the best and worst fit cases. The left

panel, Figure 8 A, illustrates a correlation between Model B predicted

tissue hemoglobin saturation 74 and the standard of comparison 72 for an

animal with a large index of correlation. The right panel, Figure 8B, illustrates the worst fit encountered. Overall, the standard error of

prediction of 6.5 was realized.

In a subset of these experiments, optode separation distance was

varied and the relative magnitude of absorbency in the deoxyhemoglobin,

oxyhemoglobin, and water features expressed against standard tissue

hemoglobin saturation (Figure 9). As expected from the modified Beer-

Lambert relationship, and as shown in Figure 9 the optical attenuation 76

attributed to each recovered constituent 78 is a linear function of optode

separation distance 80. This strongly supports the contention that h_cis a

composite parameter, dependent in part upon pathlength (see Eq 2). As

such, the absolute concentration of the recovered constituent can be

calculated using a differential pathlength factor and appropriate molar

extinction coefficient. In Figure 9, h_c for water (triangles),

oxyhemoglobin (diamonds), and deoxyhemoglobin (squares) is plotted

against the distance between the optodes at the time of measurement.

Regression analysis shows that the y-axis intercept each correlationship is

not different from the x,y axis intercept.

An advantage of using a ratioing method to compute tissue

hemoglobin saturation is illustrated in Figure 10. Tissue hemoglobin

saturation measurements are relatively insensitive to the distance between

optodes. Because tissue hemoglobin saturation is computed as a ratio, pathlength effects contained by in the numerator and denominator of the

relationship tend to cancel. The method provides a measure that is

optrode separation distance independent, i.e., accurate measurement of

optrode separation does not factor into the overall measurement error. In

the population of dogs studied, tissue hemoglobin saturation measured

using NIR technique during normoxic, normocarbic, normotensive

conditions was 60.0% YSD 11.9% and did not significantly differ from

standard tissue hemoglobin saturation (58.9% + SD 9.1%).

An alternative and preferred model, Method C, for facilitating the

spectral decomposition used to compute brain tissue hemoglobin

saturation includes several revisions to the model used for Model B.

First, the assignments of the 920 nm and 937 nm absorbencies are

reversed. In Model B, the absorbency at 920 nm is attributed to

oxyhemoglobin and the absorbency at 937 nm is attributed to a secondary

deoxyhemoglobin peak. In addition, the FWHM for the two absorbencies

is assigned values of 125 nm and 41 nm, respectively. However, after

considering results obtained from fitting whole blood spectra, in the

alternative and preferred model, these two absorbencies are reassigned to

deoxyhemoglobin and oxyhemoglobin, respectively. In addition, a 41 nm

FWHM is now applied when analyzing spectra obtained from either cat

or dog. The FWHM used to resolve the 937 nm absorbency depends on the animal species from which the spectra were collected. For cat

spectra, a value of 175 nm yields the best fit to experimental data. For

dogs, the fit is optimum when 130 nm is used to describe the peak. The

physical basis for different FWHM requirements in the two species is

unknown at this writing. These changes necessitate that the linkage

between the 760 nm absorbency and the 937 nm absorbency also be

revised, because the latter absorbency now represents oxyhemoglobin,

rather than deoxyhemoglobin.

Second, the proportionality factor coupling the primary and secondary

deoxyhemoglobin absorbencies is changed. Since the 760 and 920 nm

absorbencies now both represent deoxyhemoglobin, they are coupled in

the revised model using a proportionality factor valued at 1.09 for cats

and 1.175 for dogs. The values were obtained by re-fitting the original

data.

Third, the absorbency at 875 nm is now attributed to lipid.

Originally, the absorbency requirement at 875 nm was considered to be a

secondary peak in cytochrome c oxidase. Measurements taken from

bacon 86 and from the white matter 88 of cat brain now identify a lipid

contribution at this location. The absoφtion spectra shown in Figure 11

were collected by transilluminating a 1 cm sample chamber partially

filled with either fat from bacon, or white matter collected from cat brain. The spectra have been corrected for water content by differential

spectroscopy. Absoφtion features are notable at 879, 928, and 1038 nm

in both spectra. The FWHM are listed as determined by curve-fitting

each spectrum. The 928nm feature 90 in white matter is small in

comparison to the feature 92 observed for bacon. It should be noted that

the 879 nm features of both fat and cerebral white matter have smaller

FWHM than are needed to resolve tissue spectra. Thus, other

constituents probably overlap in this region. Possible candidates for this

region are: a secondary peak of cytochrome oxidase, or a peak of

cytochrome B.

Fourth, with the current wavelength and FWHM assignments, it is

found that the coefficients, Cj and c₂, are not required. Tissue hemoglobin

saturation is computed as a simple ratioing of the two absorbencies:

NIRHbSatbt = 100 x OD_937nm / (OD_760nm + OD₉₃₇nm). (Eq 4)

After revising the 920 and 937 nm peaks, the coupling factor

between the two deoxyhemoglobin absorbencies located at 760 nm and at

920 nm also must also be re-determined. In the re-fitting process it was

determined that the coupling factor interacts with the FWHM used to

resolve the 937 nm oxyhemoglobin peak. This interaction is a result of

the overlapping nature of these two absorbers. Increasing the value of the coupling coefficient assigns a larger portion of the oxyhemoglobin

component to the secondary deoxyhemoglobin component, thus causing

the slope of the relationship between oxyHb(_937nm THb and measured

HbSat_bt to become greater. This can be offset in part by increasing the

breadth of the oxyhemoglobin peak. This inteφlay allows a solution for

tissue hemoglobin saturation that is not dependent upon correcting

coefficients, such as Ci and c₂. Figure 12 shows the result of fitting the

fraction OD_937nm / (OD_760nm + OD₉3_7nrn) 94 to standard HbSatbt yields a fit

that lies on the line of identity and has zero bias. Also, it is seen that

Model C correlates the ratio of oxyhemoglobin 94 to total hemoglobin 96

over a range of zero to 1 , thus eliminating the need for the adjusting

coefficients C_\ and c₂. This relationship has a zero bias, but tends to yield

a somewhat larger prediction error. Unlike Model B, error appears to

correlate inversely with hemoglobin saturation level and at very low

saturation, a solution for oxyhemoglobin cannot be resolved.

The consequences of changing the coupling factor on the other

variables of the model are not obvious. The constituent most affected by

the change is total hemoglobin. In Model B, the best-fit solution to the

hemoglobin components 98 showed an increased absorbency in the

combined hemoglobin forms 102 during hypoxic-hypoxia and during

hypercapnia (Figure 13 A). This observation is consistent with the consensus of other reports. The revised model does not yield a similar

result (Figure 13B). It suggests that attenuation attributable to

hemoglobin 102 either remains unchanged, or decreases. (Figure 13B).

The fitting of spectra 98 and 100 for models B and C, respectively,

obtained during hypercarbia remain consistent between the two models.

In both cases hemoglobin attenuation 102, and by inference, cerebral

blood volume, increases. The different results reported by the two

models may be based upon Model B's inability to fully quantify

oxyhemoglobin. Because oxyhemoglobin was only partially resolved, the

rise in deoxyhemoglobin during hypoxia in combination with the

disproportionate representation of the oxyhemoglobin fall, yielded an

apparent rise in total hemoglobin. The physiological literature, currently,

does not offer an explanation for these differences. In support of the

findings from the revised Model C, there is now a suggestion that the

increase in blood flow during hypoxia is mediated through changing

blood flow velocity, rather than volume changes. The data are consistent

with this hypothesis.

Referring again to Figures 13 A and B, the linkage needed to

stabilize cross-talk between oxy-and deoxyhemoglobin biases the total

hemoglobin parameter as an index of blood volume. Hypercapnia

increases tissue hemoglobin saturation, because, by increasing cerebral blood flow, oxygen delivery is increased with respect to oxygen

consumption. In hypoxia, the resulting decrease in oxygen delivery to

tissue stimulates a compensatory increase in cerebral blood flow that

serves to buffer the change. In both instances, the rise in cerebral blood

flow would be expected to be accompanied by corresponding increases in

cerebral blood volume, and total hemoglobin content of the tissue. In

both Figures 13A and 13B above, hypercapnia was the predominant

cause for tissue hemoglobin saturations lying above 58%, and hypoxia

was the predominant cause for tissue hemoglobin saturations lying below

58%. Thus, one expects a concave relationship with a minima at the

normocapnic-normoxic value, 58%. This was observed when Model B

parameters were used to recover constituent attenuations. However, by

removing the artifact of cross-talk between oxyhemoglobin and

deoxyhemoglobin via the 760 to 920 nm linkage, it is now seen that a

tilting is introduced into the relationship such that hypoxia is not

accompanied by blood volume increases.

The method of the present invention has application to other types

of tissue besides brain tissue. Figures 14 and 15 illustrate the general

nature of the model. Figure 14 shows an absoφtion spectrum 104

collected from the head of a cat and a Model C fit 106 to such spectrum.

The overlay of the Model C fitted absoφtion spectrum 106, was reconstructed from the absoφtion coefficients that were recovered during

the fitting process. Brain spectra contain two notable features: a peak

centered around 760 nm that is attributable to deoxyhemoglobin, and a

peak centered around 972 nm that is attributable to water. Decomposition

analysis, recovers information on eight additional constituent features,

and in the process yields an excellent fit to the overall absoφtion

spectrum. While the model was developed to decompose brain spectra, it

works equally well when applied to absoφtion spectra collected from

other tissues.

Figure 15 shows an absoφtion spectrum 108 collected from human

gastrocnemius muscle and a Model C fit 110 to such spectrum. Despite

the shape differences between these two spectra (brain and skeletal

muscle), Model C was able to decompose spectra from skeletal muscle

because the constituents of the two tissues are similar. The spectral

features used to compute tissue hemoglobin saturation again include

oxyhemoglobin, deoxyhemoglobin, water, lipid, cytochrome oxidase and

a component for characterizing light loss due to scattering. Thus, it is

expected that decomposition analysis will provide much additional

information about hemoglobin saturation, hemoglobin content, lipid

content, cytochrome oxidase redox state, and water content in a variety of

organ specific tissues. In addition to the features seen in brain tissue absoφtion spectra, absoφtion spectra contains a prominent triglyceride

peak centered around 928 nm. Because the attributes of lipids and fats

are included, Model C is also able to recover constituent information

from this tissue. Hence, the method appears to be generally applicable

across a variety of tissues and, it is expected, will find application in

studies yet to be conceived.

Although the present invention has been described in terms of a

particular embodiment, it is not intended that the invention be limited to

that embodiment. Modifications of the disclosed embodiment within the

spirit of the invention will be apparent to those skilled in the art. The

scope of the present invention is defined by the claims that follow.

Claims

WHAT IS CLAIMED IS:

1. A method of measuring tissue oxygenation comprising:

collecting for said tissue a compound reflectance spectra,

decomposing said compound reflectance spectra into individual

spectral features that can be used to compute tissue hemoglobin

saturation,

using values for said spectral features attributable to

oxyhemoglobin and deoxyhemoglobin to construct a ratio for quantifying

the percentage of total hemoglobin that contains oxygen.

2. The method of measuring tissue oxygenation recited in

claim 1 wherein said spectral features used to compute tissue hemoglobin

saturation include said oxyhemoglobin and deoxyhemoglobin and water,

lipid, cytochrome oxidase and a component for characterizing light loss

due to scattering.

3. The method of measuring tissue oxygenation recited in

claim 1 wherein the step of decomposing said compound reflectance spectra into individual spectral features further includes using for said

decomposition a rule set.

4. The method of measuring tissue oxygenation recited in

claim 1 wherein the step of decomposing said compound reflectance

spectra into individual spectral features further includes using for said

decomposition a component for characterizing light loss due to scattering.

5. The method of measuring tissue oxygenation recited in

claim 3 wherein the step of using the rule set includes using a first rule

indicating that a first portion of the deoxyhemoglobin contribution to the

feature overlapping the oxyhemoglobin contribution to the feature is a

constant fraction of a second, non-overlapping of the deoxyhemoglobin

contribution to the feature.

6. The method of measuring tissue oxygenation recited in

claim 5 wherein the step of using the rule set further includes using a

second rule indicating that the deoxyhemoglobin feature at a first

wavelength changed proportionately with the deoxyhemoglobin feature at

a second wavelength.

7. The method of measuring tissue oxygenation recited in claim

6 wherein the step of using the rule set further includes using a third rule indicating that a deoxyhemoglobin feature at a third wavelength and a

oxyhemoglobin feature at a fourth wavelength do not contribute

significantly to said compound reflectance spectra using light energy in

an NIR band.

8. The method of measuring tissue oxygenation recited in

claim 1 wherein the step of collecting for said tissue a compound

reflectance spectra is done using light energy in an NIR band.

9. The method of measuring tissue oxygenation recited in claim

1 wherein the step of constructing a ratio for quantifying the percentage

of total hemoglobin that contains oxygen further includes deriving a

prediction equation for tissue hemoglobin saturation, said prediction

equation being:

OD920n ^~ Cι(OD₉₂0nm + OD₇60nm)

NIRHbSat_hf = .

10. The method of measuring tissue oxygenation recited in claim

1 wherein the step of constructing a ratio for quantifying the percentage

of total hemoglobin that contains oxygen further includes deriving a

prediction equation for tissue hemoglobin saturation, said prediction

equation being: NIRHbSatbt = 100 x OD_937nm / (OD_760nm + OD₉37nm).

11. The method of measuring tissue oxygenation recited in claim

1 wherein the step of collecting for said tissue a compound reflectance

spectra is performed using a first optode for transmitting light to said

tissue and a second optode for collecting light exiting from said tissue.

12. The method of measuring tissue oxygenation recited in claim

1 wherein the step of decomposing said compound reflectance spectra

into individual spectral features is performed using a computer.

13. A method of measuring brain tissue oxygenation comprising:

collecting for said brain tissue a compound reflectance spectra using light

energy in an NIR band,

determining for said brain tissue a model system including spectra

feature database determined by decomposing said compound reflectance

spectra into individual spectral features that can be used to compute tissue

hemoglobin saturation,

using values for said spectral features attributable to

oxyhemoglobin and deoxyhemoglobin to construct a ratio for quantifying

the percentage of total hemoglobin that contains oxygen.

14. The method of measuring tissue oxygenation recited in

claim 13 wherein said model system further includes a rule set and a

definition regarding how light scattering in said tissue contributes to

baseline absoφtion of said light energy by said brain tissue.

15. The method of measuring tissue oxygenation recited in

claim 13 wherein the spectral features used to compute tissue hemoglobin

saturation include said oxyhemoglobin and deoxyhemoglobin and water,

lipid, and cytochrome oxidase.

16. The method of measuring tissue oxygenation recited in

claim 14 wherein said rule set includes a first rule indicating that the

deoxyhemoglobin contribution to the feature overlapping oxyhemoglobin

will be a constant fraction of the non-overlapping feature.

17. The method of measuring tissue oxygenation recited in claim

16 wherein said rule set further includes a second rule using a

proportionality factor to account for the coupling of a primary

deoxyhemoglobin feature at a first wavelength with and a secondary

deoxyhemoglobin feature at a second wavelength.

18. The method of measuring tissue oxygenation recited in claim

17 wherein said second rule is defined as h_937nm = och₇₆₀nm, where 937 nm is the wavelength of the secondary deoxyhemoglobin feature, 760 nm is a

wavelength of the primary deoxyhemoglobin feature, and is a

proportionality constant.

19. The method of measuring tissue oxygenation recited in claim

17 wherein said rule set further includes a third rule indicating that a

deoxyhemoglobin feature at a third wavelength and a oxyhemoglobin

feature at a fourth wavelength do not contribute significantly to said

compound reflectance spectra using light energy in said NIR band.

20. The method of measuring tissue oxygenation recited in claim

17 wherein said rule set further included a fourth rule wherein an

absorbency at 875 nm is attributed to lipid.

21. The method of measuring tissue oxygenation recited in claim

13 wherein the step of constructing a ratio for quantifying the percentage

of total hemoglobin that contains oxygen further includes deriving a

prediction equation for tissue hemoglobin saturation, said prediction

equation being:

OD₉₂0nm - C₁(OD₉₂0nm + OD₇₆0nm)

NIRSat_ht = .

22. The method of measuring tissue oxygenation recited in claim

13 wherein the step of constructing a ratio for quantifying the percentage

of total hemoglobin that contains oxygen further includes deriving a

prediction equation for tissue hemoglobin saturation, said prediction

equation being:

NIRHbSat_bt = 100 x OD_937nm / (OD_760nm + OD_937nm).

23. The method of measuring tissue oxygenation recited in

claim 13 wherein a first absorbency at 920 nm is attributed to

oxyhemoglobin and a second absorbency at 937 nm is attributed to a

secondary deoxyhemoglobin peak.

24. The method of measuring tissue oxygenation recited in

claim 23 wherein the first absorbency is assigned a 125 nm FWHM and

the second absorbency is assigned a 41 nm FWHM.

25. The method of measuring tissue oxygenation recited in

claim 13 wherein a first absorbency at 920 nm is attributed to a secondary

deoxyhemoglobin and a second absorbency at 937 nm is attributed to

oxyhemoglobin peak.

26. The method of measuring tissue oxygenation recited in

claim 25 wherein the first absorbency is assigned a 41 nm FWHM and

the second absorbency is assigned a 125 nm FWHM.

27. The method of measuring tissue oxygenation recited in claim

13 wherein the step of collecting for said tissue a compound reflectance

spectra is performed using a first optode for transmitting said energy to

said tissue and a second optode for collecting said energy exiting from

said tissue.

28. The method of measuring tissue oxygenation recited in claim

13 wherein the steps of determining said model system and constructing

said ratio are performed using a computer.

29. An apparatus for measuring tissue oxygenation comprising:

means for collecting for said tissue a compound reflectance spectra,

means for decomposing said compound reflectance spectra into

individual spectral features that can be used to compute tissue

hemoglobin saturation, means for using values for said spectral features attributable to

oxyhemoglobin and deoxyhemoglobin to construct a ratio for quantifying

the percentage of total hemoglobin that contains oxygen.

30. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said spectral features used to compute tissue

hemoglobin saturation include said oxyhemoglobin and

deoxyhemoglobin and water, lipid, cytochrome oxidase and a component

for characterizing light loss due to scattering.

31. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein the means for decomposing said compound reflectance

spectra into individual spectral features further includes a rule set that is

used for said decomposition.

32. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein the step of decomposing said compound reflectance

spectra into individual spectral features further includes using for said

decomposition a component for characterizing light loss due to scattering.

33. The apparatus for measuring tissue oxygenation recited in

claim 31 wherein said decomposition of said compound reflectance

spectra into individual spectral features is for brain tissue and wherein said rule set includes a first rule indicating that a first portion of the

deoxyhemoglobin contribution to the feature overlapping the

oxyhemoglobin contribution to the feature is a constant fraction of a

second, non-overlapping of the deoxyhemoglobin contribution to the

feature.

34. The apparatus for measuring tissue oxygenation recited in

claim 33 wherein said rule set further includes a second rule indicating

that the deoxyhemoglobin feature at a first wavelength changed

proportionately with the deoxyhemoglobin feature at a second

wavelength.

35. The apparatus for measuring tissue oxygenation recited in

claim 34 wherein said rule set further includes a third rule indicating that

a deoxyhemoglobin feature at a third wavelength and a oxyhemoglobin

feature at a fourth wavelength do not contribute significantly to said

compound reflectance spectra using light energy in an NIR band.

36. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said collection for said tissue of a compound

reflectance spectra is done using light energy in an NIR band.

37. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said spectral features used to compute tissue

hemoglobin saturation are for brain tissue and wherein said means for

constructing said ratio for quantifying the percentage of total hemoglobin

that contains oxygen further includes means for deriving a prediction

equation for tissue hemoglobin saturation, said prediction equation being:

OD₉20nm - Cι(OD 20nm + OD ₆0nm)

NIRHbSat_bt = .

38. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said spectral features used to compute tissue

hemoglobin saturation are for brain tissue and wherein means for

constructing a ratio for quantifying the percentage of total hemoglobin

that contains oxygen further includes means for deriving a prediction

equation for tissue hemoglobin saturation, said prediction equation being:

NIRHbSatbt = 100 x OD_937nm / (OD_760nm + OD_937nm).

39. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said means for collecting said tissue a compound reflectance spectra includes a first optode for transmitting light to said

tissue and a second optode for collecting light exiting from said tissue.

40. The apparatus for measuring tissue oxygenation recited in

claim 29 wherein said means for decomposing said compound reflectance

spectra into individual spectral features is a computer.