Development and validation of a multiwavelength spatial domain near-infrared oximeter to detect cerebral hypoxia-ischemia

Lindsey A. Nelson; John C. McCann; Andreas W. Loepke; Jun Wu; Baruch Ben-Dor; Dean C. Kurth M.D.

doi:10.1117/1.2393251

1 November 2006 Development and validation of a multiwavelength spatial domain near-infrared oximeter to detect cerebral hypoxia-ischemia

Lindsey A. Nelson, John C. McCann, Andreas W. Loepke, Jun Wu, Baruch Ben-Dor, Dean C. Kurth M.D.

Author Affiliations +

Journal of Biomedical Optics, Vol. 11, Issue 6, 064022 (November 2006). https://doi.org/10.1117/1.2393251

Abstract

Detection of cerebral hypoxia-ischemia in infants remains problematic, as current monitors in clinical practice are impractical, insensitive, or nonspecific. Our study develops a multiwavelength spatial domain construct for near-infrared spectroscopy (NIRS) to detect cerebral hypoxia-ischemia and evaluates the construct in several models. The NIRS probe contains photodiode detectors 2, 3, and 4 cm from a three-wavelength, light-emitting diode. A construct determines cerebral O₂ saturation based on spatial domain principles. Device performance and construct validity are examined in in-vitro models simulating the brain, and in piglets subjected to hypoxia, hypoxia-ischemia, and hyperoxic conditions using a weighted average of arterial and cerebral venous O₂ saturation measured by CO-oximetry. The results in the brain models verify key equations in the construct and demonstrate reliable performance of the device. In piglets, the device measures cerebral O₂ saturation with bias ±4% and precision ±8%. In conclusion, this NIRS device accurately detects cerebral hypoxia-ischemia and is of a design that is practical for clinical application.

1. Introduction

Despite advances in pediatrics over the years, brain damage from hypoxia-ischemia continues to occur.^{1, 2} Populations at risk of cerebral hypoxia-ischemia include those with congenital heart disease, sickle cell anemia, cerebrovascular disease, and critical illnesses such as prematurity and sepsis. The gold standard to monitor for cerebral hypoxia-ischemia is the neurological examination. However, in these populations, this clinical exam is not reliable because of brain immaturity, systemic illness, or use of sedative drugs. Thus, the diagnosis of cerebral hypoxia-ischemia is often delayed for days to weeks, making it impossible to prevent or treat brain damage.^{1, 2}

Several technologies exist to monitor the brain, which could be used to detect cerebral hypoxia-ischemia.^{3, 4, 5} Electroencephalography and cerebral function monitors (CFM) are robust and noninvasive technologies, although they are insensitive or nonspecific for hypoxia-ischemia in the immature, sedated, or anesthetized brain. Jugular bulb oximetry, magnetic resonance imaging, and positron emission tomography are sensitive to cerebral hypoxia-ischemia; however, they are either impractical or unsafe in critically ill infants. Near-infrared spectroscopy (NIRS) is noninvasive and can be applied at the bedside. It uses near-infrared light (700 to $900 nm$ ) to monitor oxygenated and deoxygenated hemoglobin in gas exchanging vessels and can detect cerebral hypoxia-ischemia. ^{6, 7, 8, 9, 10, 11, 12}

Although NIRS holds promise for detecting cerebral hypoxia-ischemia in infants, NIRS has not been used in clinical practice because it has not been able to provide a number that is clinically relevant, accurate, and/or reliable to diagnose cerebral hypoxia-ischemia.^{13, 14} In the past few years, cerebral $O_{2}$ saturation has been identified as a number that is clinically relevant, and methods to verify NIRS accuracy have been established. ^{6, 7, 8, 9, 12} Engineering advances should make it possible to improve the reliability and accuracy of NIRS in the clinical environment. ^{9, 13, 14, 15} The present study was performed to develop and test a NIRS cerebral oximeter that has hardware features to determine cerebral $O_{2}$ saturation in infants.

2. Methods

2.1.

Algorithm

As with other forms of oximetry, NIRS relies on the Beer-Lambert law, which describes a relationship between light behavior and concentration of a compound:

Eq. 1

\log (I ∕ I_{o}) = A + S = (ε_{λ} L C) + S,

where

I

is the measured power of light at the detector after it passes through the tissue, and

I_{o}

is the measured power of light at the emitter before it enters the tissue.

S

represents light loss from scattering by the tissue, whereas

A

represents light loss from absorption by a compound in the tissue.

ε_{λ}

is the wavelength-dependent molar absorption coefficient of the absorbing compound,

L

is the path length of the light from emitter to detector, and

C

is the concentration of the absorbing compound in the tissue.

For the brain, the light absorbing compounds are mainly oxyhemoglobin $({HbO}_{2})$ and deoxyhemoglobin (Hb), and to a much lesser extent, water and cytochrome ${aa}_{3}$ .¹⁶ If multiple compounds that absorb light are present in the tissue, the total absorbance is the sum of the absorbance by all the absorbing compounds. Thus, Eq. 1 can be transformed to

Eq. 2

\log (I ∕ I_{o}) = ε^{Hb} L (Hb) + ε^{{HbO}_{2}} L ({HbO}_{2}) + A^{o} + S,

where

ε^{Hb}

and

ε^{{HbO}_{2}}

are the molar extinction coefficients of oxy- and deoxyhemoglobin, and

A^{o}

is the absorption from other compounds in the tissue (e.g., water, cytochrome

{aa}_{3}

).

$S_{O 2}$ , an index of tissue oxygenation, is defined in terms of ${HbO}_{2}$ and Hb and ${Hb}_{total}$

Eq. 3

S_{O 2} = {HbO}_{2} ∕ (Hb + {HbO}_{2}) = {HbO}_{2} ∕ {Hb}_{total},

where

{Hb}_{total}

is total hemoglobin concentration per volume of tissue, distinct from blood hemoglobin concentration, which is total hemoglobin concentration per volume of blood. Combining Eqs. 2, 3 yields

Eq. 4

\log (I ∕ I_{o}) = ε^{Hb} L ({Hb}_{total} - S_{O 2} {Hb}_{total}) + ε^{{HbO}_{2}} L (S_{O 2} {Hb}_{total}) + G,

where

G

represents

A^{o}

and

S

, as these terms can be considered one and the same with respect to the measure of

I

by an instrument. Equation 4 contains unknown variables (

L

,

S_{O 2}

,

{Hb}_{total}

, and

G

), measured variables (

I_{o}

and

I

), and known constants (

ε^{{HbO}_{2}}

and

ε^{Hb}

). It is possible to solve for

S_{O 2}

through a multiequation technique in which equations are constructed using the domains of wavelength, time, frequency, or space. Our approach combines the wavelength and spatial domains.

In the wavelength domain, the emitter and detector distance is held constant and the wavelengths are allowed to vary. For a given wavelength pair at a given emitter and detector distance, assuming constancy of $G$ between the wavelengths, Eq. 4 can be simplified to

Eq. 5

R = a L {Hb}_{total} + b L {Hb}_{total} S_{O 2},

where

a

and

b

are lump constants for the molar extinction coefficients of Hb and

{HbO}_{2}

at the two wavelengths, and

R

is the ratio of the measured intensities at the two wavelengths. Of note, Eq. 5 represents a linear function between

R

and

S_{O 2}

for any given wavelength pair and emitter-detector distance.

In the spatial domain, the wavelength is held constant and the emitter and detector distance is allowed to vary. Path length and emitter-detector distance $(D)$ are related through a constant known as the differential path length factor $(F)$

Eq. 6

L = D F .

For a given detector pair measuring light intensity from a single emitter, assuming constancy of Hb and

{HbO}_{2}

in the optical field, Eq. 2 can be developed to yield

Eq. 7

r = L_{1} (ε^{Hb} Hb + ε^{{HbO}_{2}} {HbO}_{2}) - L_{2} (ε^{Hb} Hb + ε^{{HbO}_{2}} {HbO}_{2}),

where

r

is the ratio of the measured intensities at the two detectors and

L_{1}

and

L_{2}

are optical path lengths. If the wavelength at which

I

is measured such that

ε^{Hb}

and

ε^{{HbO}_{2}}

are equal (isobestic point), then Eq. 7 can be simplified to

Eq. 8

r = ε^{Hb} {Hb}_{total} (L_{1} - L_{2}) .

Combining Eqs. 6, 8 yields

Eq. 9

{Hb}_{total} = r ∕ (ε^{Hb} F Δ D),

where

Δ D

represents the distance between the detector pair. Of note, a linear relationship exists between

r

and

{Hb}_{total}

.

Combining the wavelength and spatial domains expressed by Eqs. 5, 9 and then simplifying gives

Eq. 10

R = r A D ∕ Δ D + r B D ∕ Δ D S_{O 2},

in which

A

and

B

denote lump constants for the extinction coefficients of Hb and

{HbO}_{2}

. Using Eq. 10, it is possible to construct an algorithm to determine

S_{O 2}

by measuring light intensities at two or more detectors separated by different distances from a light source emitting at two or more wavelengths, in which one wavelength is at the isobestic point.

Boundary conditions around the emitter and detector can also affect optical pathlength and light intensity to create error or noise in the measurement of $S_{O 2}$ from $D$ , $R$ , and $r$ .^{17, 18} Several methods exist to control this effect, including the use of light shields around the emitter, detector, and tissue; the use of materials and designs to improve coupling between the emitter, detector, and tissue; and the application of robust algorithms and signal processing. In our experience, no single method has been satisfactory. Thus, our approach relies on the design of an optical probe housing the emitter and detector to enhance light shielding and light-tissue coupling, as well as algorithm and signal processes to reduce aberrant signals.

For the algorithm, $S_{O 2}$ is determined from the average of several wavelength pairs and emitter-detector distances. If Eq. 10 is solved for $S_{O 2}$ , in which the detector pair to determine $r$ is $1 cm$ apart $(Δ D = 1)$ , then

Eq. 11

S_{O 2} = (R - r A D) ∕ (r B D) .

Expanding Eq. 11 to include several wavelength pairs and emitter-detector distances yields an expression

Eq. 12

S_{O 2} = [\sum_{i = 1}^{N_{λ}} \sum_{j = 1}^{N_{δ}} (R_{i, j} - r_{j} A_{i} D_{j}) ∕ (r_{j} B_{i} D_{j})] ∕ N_{λ} N_{δ},

where

N_{λ}

is the number of wavelength pairs and

N_{δ}

is the number of emitter-detector distances.

2.2.

Materials

A prototype NIRS device was constructed (Near Infrared Monitoring Incorporated, Philadelphia, Pennsylvania). It consisted of a probe housing light emitters and three photodiode detectors separated from the emitter by 2, 3, and $4 cm$ . The emitter contained light emitting diodes at 730, 805, and $850 nm$ . The 805-nm wavelength is an isobestic point for oxy- and deoxyhemoglobin. The emitter and detectors were housed within a soft plastic shell, connected to the main unit by a wire bundle. The emitter and detectors were recessed within the probe to provide light shielding, while the material for the probe was lightweight and highly compliant to facilitate probe-tissue coupling. The main unit contained the electronic hardware to capture the signals at each wavelength and detector. After power-up, the device undergoes an internal calibration to check the integrity of the hardware. Application of this device to Eq. 12 results in the measurement of nine values for $R$ and one value for $r$ , each captured at $3 Hz$ and signal averaged over $10 s$ .

An in-vitro “static” brain model consisting of India ink and intralipid admixed in a gelatin polymer to mimic the optical density of the human head was used to determine instrument signal-to-noise and drift. An in-vitro “dynamic” brain model simulating the brain was used to test Eqs. 5, 9 and to develop the algorithm per Eq. 12.¹⁹ This model contained a microvascular network perfused with human blood in which $S_{O 2}$ and ${Hb}_{total}$ could be varied. Blood $S_{O 2}$ was measured by $CO$ -oximetry (OSM™ 3, Hemoximeter™, Radiometer Copenhagen, Copenhagen NV, Denmark). Blood gas analysis was performed (iSTAT, iSTAT Corporation, Princeton, New Jersey). pH and ${PCO}_{2}$ were maintained at 7.35 to 7.45 and 35 to $45 torr$ , respectively.

A piglet model was used to test the algorithm [Eq. 12] prospectively. After approval by the Institutional Animal Care and Use Committee, 13 piglets aged 4 to 6 days were studied. Anesthesia was induced with intramuscular ketamine $(33 mg ∕ kg)$ and acepromazine $(3.3 mg ∕ kg)$ , and maintained with fentanyl ( $25 μ g ∕ kg$ bolus, then $10 μ g ∕ kg ∕ h$ ) and midazolam ( $0.2 mg ∕ kg$ bolus, then $0.1 mg ∕ kg ∕ h$ ). Following tracheal intubation and mechanical ventilation, catheters were inserted into the femoral artery and superior sagittal sinus to sample blood for arterial saturation $(S a_{O 2})$ and cerebral venous saturation $(S s_{O 2})$ . In six piglets, an incision was made in the neck, the carotid arteries were isolated, and ligatures were placed around the vessels to produce cerebral ischemia.

2.3.

Protocols

In the static model, $3 h$ of NIRS data were recorded to determine static signal-to-noise and signal drift. To determine the effect of probe positioning on signal-to-noise (dynamic signal-to-noise), the probe was repositioned 15 times in the same location. NIRS data were recorded before and after each positioning. In the dynamic model, Eq. 5 was examined by increasing ${SO}_{2}$ from 0 to 100%, and blood samples from the model and NIRS data were recorded at each increment. Experiments were performed at two different blood ${Hb}_{total}$ . To test Eq. 9, ${Hb}_{total}$ was decreased from 15 to $5 g ∕ dl$ in $2 g ∕ dl$ steps, while $S_{O 2}$ was held at 70%. Blood samples from the model and NIRS data were recorded at each hemoglobin concentration.

In the piglet experiments, inspired $O_{2}$ $({FiO}_{2})$ , minute ventilation, and cerebral perfusion were varied to force cerebral $O_{2}$ saturation over a wide range during high and low cerebral blood volume conditions. Piglets were divided into a hypoxia group and a hypoxia-ischemia group, in which the hypoxia-ischemia group had both carotid arteries completely occluded during the hypoxia conditions. The following conditions were produced. 1. Normoxia: room air was inspired with minute ventilation adjusted to normocapnia. 2. Hypercapnia/hyperoxia: ${FiO}_{2}$ was 100% and minute ventilation was decreased to a 10% expired ${CO}_{2}$ . 3. Mild hypoxia: ${FiO}_{2}$ was decreased to 17%. 4. Moderate hypoxia: ${FiO}_{2}$ was decreased to 13%. 5. Severe hypoxia: ${FiO}_{2}$ was decreased to 8%. After $5 \min$ at each condition, NIRS ${SO}_{2}$ $(S c_{O 2})$ , arterial pressure, arterial blood gases, and pH, $S a_{O 2}$ and $S s_{O 2}$ were recorded. After the experiment, piglets were euthanized with intravenous pentobarbital $100 mg ∕ kg$ .

2.4.

Data Analysis

Data are presented as mean $\pm SD$ . For the static model, drift was

Eq. 13

drift = 100^{*} [(initial {Sc}_{O 2} - end {Sc}_{O 2}) ∕ initial {Sc}_{O 2}] ∕ 3 h,

where the initial and end

S c_{O 2}

represent the average

S c_{O 2}

over five minutes at the beginning and end of the 3-h experiment. Instrument static signal-to-noise and dynamic signal-to-noise were

Eq. 14

signal - to - noise = 100^{*} (SD of {Sc}_{O 2}) ∕ (average {Sc}_{O 2}),

where the average

S c_{O 2}

is the average value over

3 h

for the static signal-to-noise, and the average of the 15 values from positioning for the dynamic signal-to-noise.

For the dynamic model, linearity of $R$ and $S_{O 2}$ [Eq. 5], and $r$ and ${Hb}_{total}$ [Eq. 9], were determined by least squares regression. For the piglet model, blood $S_{O 2}$ in the cerebrovasculature may be approximated by $S m_{O 2}$ , a weighted average of $S a_{O 2}$ and $S s_{O 2}$ ^{7, 8, 13, 19}

Eq. 15

{Sm}_{O 2} = 0.85 ({Sa}_{O 2}) + 0.15 (S s_{O 2}) .

Algorithm accuracy for

S c_{O 2}

was evaluated in terms of

S m_{O 2}

by least squared regression, as well as by the bias and precision.^{20, 21} Unpaired T-tests with bonferroni correction were used to compare

S c_{O 2}

,

S a_{O 2}

, and

S s_{O 2}

between conditions.

3. Results

Table 1 displays NIRS device performance in the static brain model. Drift varied from 2%/h to $- 2 % ∕ h$ among the ratios and detectors, the average being $< 1 %$ . Static signal-to-noise ranged from 0.1 to 1.9% among the ratios and detectors, the average being $< 1 %$ . The dynamic signal-to-noise ranged from 12 to $- 9 %$ among the ratios and detectors, the average being 2.6%. The dynamic signal-to-noise was significantly less at the 4-cm emitter-detector than at the 2- and 3-cm emitter-detectors $(p < 0.001)$ . The dynamic signal-to-noise was significantly greater than the static signal-to-noise at each emitter-detector pair $(p < 0.001)$ .

Table 1

NIRS cerebral oximeter performance in a static brain model. A, B, and C represent wavelength intensity ratios for 730/ 850nm , 730/ 805nm , and 850/ 805nm , respectively. s/n is signal to noise. E-D is the distance between emitter and detector. % represents the actual ΔScO2 from drift or signal-to-noise (see Eqs. 13, 14 in Sec. 2).

E-D (cm)	2			3			4
	A	B	C	A	B	C	A	B	C	Mean
Drift $(% ∕ h)$	$- 1.0$	$- 1.2$	2.1	$- 1.8$	$- 1.7$	$- 0.2$	$- 0.9$	$- 1.4$	0.3	$- 0.6$
Static s/n (%)	0.1	0.3	1.1	0.3	0.2	1.9	0.2	0.3	0.2	0.6
Dynamic s/n (%)	12	8.0	9.0	9.0	9.0	$- 9.0$	1.0	1.0	$- 1.0$	2.6

Table 2 and Figs. 1 and 2 illustrate the dynamic brain model experiments. Linear relationships with excellent correlations were observed between $S_{O 2}$ and the intensity ratios for all wavelength pairs $(R)$ at all detectors (Table 2), verifying the relationship in Eq. 5. Slopes and intercepts increased significantly as emitter-detector distance increased (all $p < 0.01$ ), in keeping with the effect of the longer path length at the greater emitter-detector distances in Eqs. 5, 6. Although linear relationships were observed at each hemoglobin concentration (Fig. 1), the slopes and intercepts were significantly greater at the higher hemoglobin concentration compared with those at the lower hemoglobin concentration, as predicted in Eq. 5. In Fig. 2, a linear relationship was observed between hemoglobin concentration and intensity ratios for a distance pair at $805 nm$ $(r)$ , as predicted from Eq. 9.

Fig. 1

Response of the wavelength pair intensity ratio $(R)$ for 730 and $850 nm$ measured at the 4-cm detector by the NIRS oximeter, to $O_{2}$ saturation $(S_{O 2})$ of blood perfusing the brain model. There was a highly linear response, although hemoglobin concentration of the blood significantly altered it.

Fig. 2

Response of the detector pair intensity ratio $(r)$ for 1- and 2-cm detectors measured at $805 nm$ by the NIRS oximeter, to hemoglobin concentration of blood perfusing the brain model. There was a highly linear response.

Table 2

NIRS oximeter performance in a dynamic brain model. Abbreviations same as Table 1. Slope, intercept, and r2 are for the line between the wavelength pair intensity ratio [ R in Eq. 12 in Sec. 2] calculated by the device and O2 saturation (SO2) of blood perfusing the brain model.

E-D (cm)	2			3			4
	A	B	C	A	B	C	A	B	C
Ratio versus $S_{O 2}$
Slope	38	31	$- 18$	51	40	$- 24$	62	51	$- 33$
intercept	37	47	121	35	46	122	19	29	129
$r^{2}$	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.98	0.98

Tables 3, 4 list the blood gases and pH, as well as $S a_{O 2}$ , $S s_{O 2}$ , and NIRS $S c_{O 2}$ in the test piglet experiments. Arterial blood values were not significantly different between the hypoxia and hypoxia-ischemia groups, except at 17 and 9% ${FiO}_{2}$ , where $S s_{O 2}$ in the hypoxia group was significantly greater than in the hypoxia-ischemia group.

Table 3

Physiological parameters in the piglet hypoxia-ischemia model. Values are mean ± SD, n=13 . PCO2 , PO2 , and MAP represent arterial partial pressure of carbon dioxide and oxygen, and mean arterial pressure, respectively.

Condition	pH	PCO2 (torr)	PO2 (torr)	MAP (mmHg)
Normoxia	$7.47 \pm 0.06$	$36 \pm 4$	$77 \pm 8$	$86 \pm 13$
Hypercapnic-hyperoxia	$7.13 \pm 0.05$	$90 \pm 14$	$401 \pm 84$	$83 \pm 13$
Hypoxia 17%	$7.52 \pm 0.09$	$33 \pm 3$	$59 \pm 9$	$83 \pm 9$
Hypoxia 13%	$7.49 \pm 0.08$	$34 \pm 3$	$36 \pm 10$	$80 \pm 12$
Hypoxia 9%	$7.48 \pm 0.01$	$35 \pm 3$	$22 \pm 4$	$68 \pm 13$

Table 4

Arterial, cerebral venous sinus, and NIRS cerebral O2 saturation in the piglet model. Values are mean±SD . N=7 for H and n=6 for HI. H is hypoxia only; H-I is hypoxia and ischemia. p*<0.01H versus HI. SaO2 , SsO2 , and ScO2 are arterial, sagittal sinus (cerebral venous), and NIRS cerebral O2 saturation, respectively.

Condition	SaO2 (%)		SsO2 (%)		ScO2 (%)
	H	H-I	H	H-I	H	H-I
Normoxia	$96 \pm 1$	$96 \pm 3$	$40 \pm 3$	$44 \pm 9$	$55 \pm 7$	$54 \pm 5$
Hypercapnic-hyperoxia	$99 \pm 3$	$100 \pm 0$	$88 \pm 6$	$87 \pm 7$	$93 \pm 10$	$89 \pm 8$
Hypoxia 17%	$93 \pm 4$	$90 \pm 4$	$35 \pm 11$	$19 \pm 6^{*}$	$46 \pm 10$	$38 \pm 8$
Hypoxia 13%	$75 \pm 15$	$64 \pm 13$	$23 \pm 15$	$14 \pm 3$	$25 \pm 9$	$24 \pm 6$
Hypoxia 9%	$46 \pm 15$	$36 \pm 14$	$14 \pm 5$	$6 \pm 1^{*}$	$17 \pm 9$	$12 \pm 2$

Figures 3 and 4 display the device algorithm performance during the test piglet experiments. There were linear relationships with excellent correlations between the NIRS $S c_{O 2}$ and $S m_{O 2}$ (Fig. 3, $y = 0.96 - 5$ , $r^{2} = 0.92$ ) and $S s_{O 2}$ ( $y = 0.87 - 13$ , $r^{2} = 0.89$ ). The bias and precision, respectively, for the NIRS $S c_{O 2}$ was $- 4$ and 8% relative to $S m_{O 2}$ (Fig. 4), and $- 9$ and 10% relative to $S s_{O 2}$ . Bias, precision, and correlation were similar in the hypoxia and hypoxia-ischemia groups.

Fig. 3

NIRS oximeter performance in piglets subjected to hypoxia $(n = 6)$ or hypoxia-ischemia $(n = 7)$ . $S c_{O 2}$ is cerebral $O_{2}$ saturation measured by the NIRS oximeter, and $S m_{O 2}$ is the weighted average cerebral $O_{2}$ saturation [Eq. 15] measured by $CO$ -oximetry. Line indicates regression. There is a highly linear relationship.

Fig. 4

NIRS oximeter performance in the same piglets as Fig. 3. Difference $S_{O 2}$ is the difference between $S c_{O 2}$ and $S m_{O 2}$ . Dashed and dotted lines indicate bias and precision.

4. Discussion

Hypoxia-ischemic brain injury continues to occur in certain pediatric populations.^{1, 2} The present study developed a NIRS spatial domain construct to detect cerebral hypoxia-ischemia, built a device to use the construct, evaluated the device and construct in several brain models, and tested the construct-algorithm to measure cerebral $O_{2}$ saturation in a piglet hypoxia-ischemia model. The results demonstrated reliable performance of the device in brain models, verified key equations in the construct in the brain models, and observed accurate measurement of cerebral $O_{2}$ saturation with respect to the surrogate measures of $S m_{O 2}$ and $S s_{O 2}$ in the animal model.

Over the years, several issues have hampered the use of NIRS in clinical practice. These issues relate to the measure of tissue oxygenation, experimental methods to validate this measure, the availability of clinically practical devices, and identification of critical tissue oxygenation values to guide diagnosis and therapy. ^{13, 14, 15, 20} In brain tissue, the oxygen-sensitive compounds that absorb near-infrared light include hemoglobin and cytochrome ${aa}_{3}$ .¹⁶ It is therefore possible to apply NIRS to measure hemoglobin oxygenation and/or cytochrome ${aa}_{3}$ oxygenation, reflecting an assessment of the intravascular and the intracellular compartments, respectively. In theory, it is preferable to know intracellular oxygenation, because that is where oxidative metabolism occurs and insufficient oxidative metabolism ultimately leads to energy failure and cell death. Moreover, although tight coupling exists between intravascular and intracellular oxygenation, uncoupling can occur in rare situations (e.g., carbon monoxide poisoning, brain death).^{10, 11, 12} However, at the present time, cytochrome ${aa}_{3}$ measurement is technically difficult and prone to error in the clinical environment.^{22, 23, 24} Thus, a practical approach to detect cerebral hypoxia-ischemia in these rare situations is to measure hemoglobin oxygenation with neurophysiological function (eg., EEG).²³

NIRS hemoglobin oxygenation has been measured in terms of oxyhemoglobin concentration $({Hb}_{O_{2}})$ , oxy-deoxy hemoglobin concentration difference $({Hb}_{diff})$ , $O_{2}$ saturation ( $S c_{O 2}$ or $r S_{O 2}$ ), and tissue oxygenation index $({Ti}_{O_{2}})$ . ^{6, 7, 8, 9, 11, 25} Because ${HbO}_{2}$ and ${Hb}_{diff}$ are mass per volume of tissue measurements, they indicate tissue oxygenation. Because $O_{2}$ flux from blood to the mitochondria is driven by $O_{2}$ partial pressure linked to the oxy-hemoglobin dissociation curve, $S c_{O 2}$ , $r S_{O 2}$ , and ${Ti}_{O_{2}}$ indicate tissue oxygenation. For historical reasons, clinicians use $O_{2}$ saturation to describe blood oxygenation. We selected $O_{2}$ saturation as the term to describe cerebral oxygenation, because it is a clinically user-friendly term and other instruments exist to measure it (e.g., $CO$ -oximetry), which helps with validation.

Experimental methods to validate NIRS have been problematic.^{13, 20} Validation requires a comparison of the measurements made by the new device against a standard; the accuracy of the new device is then expressed in terms of bias and precision relative to the standard.^{20, 21} NIRS monitors a mixed vascular bed dominated by gas-exchanging vessels, especially venules.^{8, 26} Because no other device measures such a mixed vascular oxygenation, NIRS has lacked a standard to validate it. However, in situations where no standard exists, it is still possible to validate a device using a surrogate, or the average of two or more surrogates, which approximates the standard.²¹ In our study, we used $S m_{O 2}$ , a weighted average of $S a_{O 2}$ and $S s_{O 2}$ , as the main surrogate, and $S s_{O 2}$ alone as a minor surrogate. Not surprisingly, we observed that the bias and precision was higher against $S s_{O 2}$ , because it introduces error from the omission of the arterial contribution viewed by NIRS. Although $S m_{O 2}$ contains an arterial contribution and is better than $S s_{O 2}$ , it introduces error because it uses a constant venous: arterial ratio, which has been shown to vary slightly among subjects and conditions.⁸ Thus, some imperfection in the bias, precision, and correlation originates from the $S m_{O 2}$ surrogate. It might be possible to assess the imperfection by estimating the arterial contribution in the $S m_{O 2}$ of each piglet through the measurement of the pulsatile (arterial) component of the optical signal.

How accurate does NIRS have to be for clinical application? There are no clinical studies to answer this, although animal data suggest a precision of 10% is needed. Cerebral $O_{2}$ saturation in healthy humans and piglets is 55 to 75%.^{8, 12, 13} In piglets, after cerebral $O_{2}$ saturation decreases to 40%, brain function becomes disturbed.^{12, 13} Thus, the cushion between normal and dysfunctional in piglets is 15% at a minimum, at the precision of the device $(\pm 8 %)$ . Thus, the accuracy of the device appears sufficient to warrant clinical trials to determine application in pediatrics.

To determine $O_{2}$ saturation, it is necessary to account for the effects of light scattering and optical path length.¹⁷ Time domain, frequency domain, or spatial domain principles were developed for this purpose. We used the spatial domain because of advantages in engineering and costs over time- and frequency-domain technologies. The spatial domain requires equality of light scattering among the wavelengths and equality of $O_{2}$ saturation in the optical fields among the emitter-detectors [see Eqs. 5, 8]. The results indicate that these assumptions were reasonably met for the models employed in our study.

The limitation of spatial domain relates to the different optical paths among the emitter-detector combinations. Computer simulations have shown three possible routes for photons to take after leaving the emitter.^{17, 27} 1. shallow—these photons deflect off the scalp, skull, or neocortical surface and never make it to the detectors; 2. deep—these photons scatter indefinitely and never exit from the head; 3. middle—these photons encounter multiple scattering events before making their way to the detectors. The middle photons appear to travel within a banana-shaped field, the depth of which approximates one third the emitter-detector distance.^{18, 27} Our optical probe contained emitter-detector distances of 2, 3, and $4 cm$ , which would give penetration depths of approximately 0.6, 1, and $1.3 cm$ , corresponding to the “bananas” being in the neocortex and basal ganglia.

Considering the various optical paths, the following errors might occur with our device. First, it would not detect focal ischemia outside the optical field. For instance, a probe located on the forehead and monitoring the frontal neocortex would not detect ischemia in the occipital neocortex. Second, it would not detect highly focal ischemia within an optical field that was otherwise well oxygenated. For example, the probe would not detect laminar ischemia in white matter while the overlying gray matter was normally oxygenated. Thus, the spatial-domain device is best suited to detect global cerebral hypoxia-ischemia, as in our piglet model.

Motion artifact, drift, and other noise hinder NIRS devices from being practical, reliable, and accurate in the clinical environment. In our study, the drift and static signal-to-noise were small in comparison to the dynamic signal-to-noise, which at the individual detectors was up to $S c_{O 2}$ 12% and of a magnitude that could be troublesome in the clinical environment, as the buffer between normal and dysfunctional is approximately $S c_{O 2}$ 15%.^{8, 12, 13} Boundary conditions around the emitter and detector create this error by affecting $D$ , $R$ , and $r$ in Eq. 10.^{17, 18} We employed the use of multiple-wavelength ratios in the algorithm, longer emitter-detector distances (see Table 1), and a lightweight optical probe to facilitate optical-tissue coupling. Nevertheless, other solutions will be required to lessen this noise.

NIRS devices have employed time-, frequency-, and spatial-domain principles to determine cerebral oxygen saturation. ^{7, 8, 9, 12, 15, 18, 22, 28} In laboratory settings, studies of frequency- and time-domain devices report accuracy similar to our study.^{9, 15, 28} Studies of commercially available spatial-domain devices reveal poor agreement among the devices and less accuracy compared with our study.^{7, 18, 29}

In summary, our prototype NIRS device uses inexpensive, robust, and clinically friendly technology similar to pulse oximetry. The device uses a multiwavelength spatial domain construct and is sufficiently accurate to diagnose cerebral hypoxia-ischemia in piglets. Before clinical use, human studies to validate the device and optical probe design work are indicated.

Acknowledgments

The authors wish to thank Melinda Chappell for editorial assistance. This work was supported by National Institutes of Health grant R42 NS039707.

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Citation Download Citation

Lindsey A. Nelson, John C. McCann, Andreas W. Loepke, Jun Wu, Baruch Ben-Dor, and Dean C. Kurth M.D. "Development and validation of a multiwavelength spatial domain near-infrared oximeter to detect cerebral hypoxia-ischemia," Journal of Biomedical Optics 11(6), 064022 (1 November 2006). https://doi.org/10.1117/1.2393251

Published: 1 November 2006

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KEYWORDS

Oxygen

Near infrared spectroscopy

Sensors

Brain

Blood

Hypoxia

Signal to noise ratio

1.

Introduction

2.

Methods

2.1.

Algorithm

Eq. 1

Eq. 2

Eq. 3

Eq. 4

Eq. 5

Eq. 6

Eq. 7

Eq. 8

Eq. 9

Eq. 10

Eq. 11

Eq. 12

2.2.

Materials

2.3.

Protocols

2.4.

Data Analysis

Eq. 13

Eq. 14

Eq. 15

3.

Results

Table 1

Fig. 1

Fig. 2

Table 2

Table 3

Table 4

Fig. 3

Fig. 4

4.

Discussion

Acknowledgments

References

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