Voxel-based measurement sensitivityof spatially resolved near-infrared
spectroscopy in layered tissues
Masatsugu Niwayama

Masatsugu Niwayama, “Voxel-based measurement sensitivity of spatially resolved near-infrared spectroscopy
in layered tissues,” J. Biomed. Opt. 23(3), 030503 (2018), doi: 10.1117/1.JBO.23.3.030503.
Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 02 Dec 2019
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

JBO Letters

Voxel-based measurement sensitivity ofspatially resolved nearinfrared spectroscopy
in layered tissues
Masatsugu Niwayama*
Shizuoka University, Faculty of Engineering, Department of Electricaland Electronic Engineering, Hamamatsu, Japan

Abstract. We quantitatively investigated the measurement sensitivity of spatially resolved spectroscopy (SRS)
across six tissue models: cerebral tissue, a small animal
brain, the forehead of a fetus, an adult brain, forearmmuscle, and thigh muscle. The optical path length in thevoxel of the model was analyzed using Monte Carlo simulations. It was found that the measurement sensitivity canbe represented as the product of the change in the absorption coefficient and the difference in optical path length intwo states with different source–detector distances. The
results clarified the sensitivity ratio between the surface
layer and the deep layer at each source–detector distancefor each model and identified changes in the deep measurement area when one of the detectors was close to thelight source. A comparison was made with the results fromcontinuous-wave spectroscopy. The study also identified
measurement challenges that arise when the surface
layer is inhomogeneous. Findings on the measurement
sensitivity of SRS at each voxel and in each layer can support the correct interpretation of measured values whennear-infrared oximetry or functional near-infrared spectroscopy is used to investigate different tissue structures. © The
Authors. Published by SPIE under a Creative Commons Attribution 3.0
Unported License. Distribution or reproduction of this work in whole or inpart requires full attribution of the original publication, including its DOI.
[DOI: 10.1117/1.JBO.23.3.030503]

Keywords: near-infrared spectroscopy; oximetry; spatially resolved
spectroscopy; voxel; sensitivity.
Paper 170729LR received Nov. 12, 2017; accepted for publication
Feb. 21, 2018; published online Mar. 9, 2018.

1

Introduction

Tissue oximetry using near-infrared spectroscopy (NIRS) is
used in a range of fields, including brain research, sports medicine, surgery, and obstetrics.1,2 Implantable devices and devices
mounted on the finger of the investigator have been developed,3,4 and the range of tissue types that can be examined has
widened. NIRS instruments apply four basic techniques: timeresolved spectroscopy (TRS), spatially resolved spectroscopy(SRS), frequency-domain spectroscopy (FDS), and continuouswave spectroscopy (CWS). TRS, FDS, and CWS record*Address all correspondence to: Masatsugu Niwayama, E-mail: niwayama.
masatsugu@shizuoka.ac.jp

Journal of Biomedical Optics

changes in light intensity at a single point, whereas SRS isa multipoint approach. CWS, which uses an adaptation of theBeer–Lambert law, is the simplest to apply and the most widelyused. Recent TRS5 using data processing methods and high-performance FDS6 have been developed to measure scattering andabsorption coefficients, but these systems have not been widely
introduced to clinical tests because it is a complex and expensive
system. Both SRS and CWS are simple and low-cost approaches
to light radiation and detection and benefit from a high signal-tonoise ratio. SRS is used to measure the absolute values of hemoglobin concentration, and its measurement sensitivity can besuppressed when examining superficial tissue.7 This is because
analysis is based on the difference in intensity at two points.
A number of previous studies have addressed the spatial sensitivity distribution of CWS8–11 and diffuse optical tomography(DOT).12,13 Dehghani et al.12 and Saikia and Kanhirodan13
analyzed the influence of source–detector separation and depthrelated sensitivity for reconstruction of DOT. However, as its
voxel-based sensitivity for SRS oximetry has not yet been
quantified, there is a need for further investigation of theroles played by source–detector separation and by the various
layered structures of the target tissue. In the current study,
we examined the SRS sensitivity in the source–detector axis
direction and depth direction and compared the difference inmeasurement sensitivity of SRS and CWS at each voxel
and layer.

2

Methods

On the basis of radiative transfer theory, we performed a Monte
Carlo analysis of the six tissue types shown in Fig. 1. For the
directly contacted cerebral tissue and the small animal brain, asource–detector separation of up to 20 mm was used. This wasincreased to 40 mm when tested on the fetus, adult head, andlimb muscles, mimicking the separation used by commerciallyavailable instruments or reported by research studies.1–4 Figure 1
shows the six model tissues used in the Monte Carlo analysis:(1) cerebral tissue contacted directly by an optical probe, (2) asmall animal brain accessed via the scalp, (3) the forehead ofa fetus, (4) an adult brain, (5) forearm muscle, and (6) thighmuscle. The optical properties shown in Table 1 and used in
the simulations were based on previous literature.14–19 To allow
the simulation results to be applied to NIRS data interpretation,
the models were divided into a surface part and a deep part.
Figure 1(a) shows a surface to deep combination of gray matterand white matter, Figs. 1(b)–1(d) show the scalp plus graymatter and white matter, and Figs. 1(e) and 1(f) show skin andmuscle. The thickness of the surface component was set to
1.5 mm to represent the scalp and skin thickness of 1 to
2 mm.20 The sensitivity of CWS is known to be determined by
the mean optical path length L. The relationship between path
length and SRS sensitivity is as follows. I A is the light intensity
detected over a short distance ρA from the light source, and I B is
the intensity detected over a longer distance ρB . As the spatialintensity slope S is the difference in light intensity per unit
length, S is defined as lnðI A ∕I B Þ. The change in S given achange in the absorption coefficient μa is then defined as themeasurement sensitivity of SRS. The values of S are takenfrom optical measurements, and the unknown absorption coefficients are derived from the values of S and the theoreticalcurves.
In Eq. (1), suffixes 1 and 2 denote values before and after thesmall change in absorption, respectively. The ΔS associated030503-1

Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 02 Dec 2019
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

March 2018 • Vol. 23(3)JBO Letters
(a)

(d)

(b)

(e)

(a)

(b)

(c)

(f)

Fig. 1 Simulation models for (a) cerebral tissue, (b) small animal
brain, (c) fetal brain, (d) adult brain, (e) forearm muscle, and(f) thigh muscle.
Table 1 Optical properties for each tissue.

Fig. 2 SRS sensitivity distribution from an adult head model:(a) ρA ¼ 7 mm, ρB ¼ 35 mm and (b) ρA ¼ 30 mm, ρB ¼ 35 mm.

values in the same layer. The SRS sensitivity for the surface
and deep layers is derived as follows:

ΔSsurface ¼ ΔμsurfaceðLsurface− Lsurface
Þ;
a
B
A

(3)

deep
ΔSdeep ¼ Δμdeep− Ldeep
a ðLB
A Þ:

(4)

EQ-TARGET;temp:intralink-;e003;326;505

TissueReduced scattering
coefficient μs0 (mm−1 )Absorption
coefficient μa (mm−1 )

Scalp

1.3

0.020

Skull

2.0

0.010

Cerebrospinal fluid

0.3

0.002

Gray matter

1.6

0.035

White matter

5.0

0.015

Skin

1.3

0.020

Fat

1.2

0.003

Muscle0.7

0.025

EQ-TARGET;temp:intralink-;e004;326;475

For example, Lsurface
denotes the intensity-weighted averageApath length through the surface layer that the light detected
at a position A has traversed. To clarify the influence of thechange of S on the oximetry, the absorption coefficient iscalculated by the commonly used equation 21 as follows:


2
1
S
2
μa ≈ 0−:B
3μs ρB − ρA ρA þρ
2

EQ-TARGET;temp:intralink-;e005;326;392

The concentrations of oxyhemoglobin [O2 Hb], deoxyhemoglobin [HHb], and tissue oxygen saturation StO2 are obtained by
λ1
λ1
λ2
ελ2
HHb μa − εHHb μa
;
λ2
λ2
λ1
ελ1
O2 Hb εHHb − εO2 Hb εHHb

(6)

λ1
λ1
λ2
ελ2
O Hb μa − εO2 Hb μa½HHb ¼ − λ1 2 λ2;λ2λ1εO2 Hb εHHb − εO2 Hb εHHb

(7)

½O2 Hb;½O2 Hb þ ½HHb

(8)½O2 Hb ¼

EQ-TARGET;temp:intralink-;e006;326;327

with a change in optical density ΔOD and the mean path lengths
LA and LB is then derived as follows:

ΔS ¼ lnðI 2A ∕I 2B Þ − lnðI 1A ∕I 1B Þ

EQ-TARGET;temp:intralink-;e001;63;272

EQ-TARGET;temp:intralink-;e007;326;283¼ lnðI 2A ∕I 1A Þ − lnðI 2B ∕I 1B Þ
StO2 ¼¼ −ΔODA þ ΔODB
¼ Δμa ðLB − LA Þ:

EQ-TARGET;temp:intralink-;e008;326;240(1)

In CWS, sensitivity is usually based on the mean path length,
whereas in SRS the sensitivity is also influenced by the difference between LA and LB . On the basis of this relationship, thesensitivity of SRS for a small voxel at point (x; y; z) is given byx;y;zΔSx;y;z ¼ Δμx;y;z− Lx;y;z
a ðLB
A Þ:

EQ-TARGET;temp:intralink-;e002;63;147(2)

By preliminary analysis to investigate the slope change due toan increase in the whole μa or the voxel μa by 1%, it was verified
that Eqs. (1) and (2) held when Δμa was small and the relationship between S and μa could be regarded as linear. The sensitivity for each layer is derived by integrating the voxel
Journal of Biomedical Optics

(5)

λ1;λ2
where εOand ελ1;λ2
HHb are the extinction coefficients of HHb and2 Hb
O2 Hb, respectively, at the wavelengths λ1 and λ2 .22 In this study,
the influence when the position of the absorber changes inthe direction of the source–detector axis was also analyzed.
We assumed two wavelengths of 770 and 830 nm and placed
a 10 − ×10 − ×3.5-mm hypoxic region (StO2 : 30%, μa770 ¼
0.044 mm−1 , μa830 ¼ 0.032 mm−1 ) in the normal cortex (StO2 :63%, μa770 ¼ 0.035 mm−1 , μa830 ¼ 0.035 mm−1 ).

3

Results and Discussion

Figure 2 shows the voxel-based sensitivity ΔSx;y;z from twomodel detector combinations: ρA ¼ 7 mm, ρB ¼ 35 mm, and030503-2

Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 02 Dec 2019
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

March 2018 • Vol. 23(3)JBO Letters
In both Figs. 2(a) and 2(b), the sensitivity of the surface layer
was negative close to detector A and positive close to detector B.
When blood is in the negative sensitivity area, an incorrect
reverse tendency will be mixed into the calculation results of
the oxygen saturation and blood volume. At a ρA of 30 mm,
a strong positive sensitivity was noted within a narrow range inthe deep layer, but a small negative sensitivity appeared at theside near the light source. Figure 3 shows the oxygen saturation
due to the change in the relative position between the opticalprobe and the 30%-StO2 region. When light was detected ina pair of 7 to 35 mm, StO2 decreased by 4.5% with the approachof the low oxygen tissue. When a pair of 30 to 35 mm was used,
it decreased by 8%, but StO2 at x ¼ −5 mm increased by 1%
despite low oxygenation. These results suggest the following:(1) when the surface layer is heterogeneous, as is the casefor veins, wounds, or inflammation, the effect on the measured
value is large and can be positive or negative. (2) When both
detectors A and B are distant from the light source, manysites have negative sensitivity, but information can be obtained
from a narrow region of the deep layer. (3) When the gap
between ρA and ρB is sufficiently long, average information
can be obtained across a wide area of the deep tissue.
Figure 4 shows the measurement sensitivity of each layerfrom every model for both CWS and SRS. Because SRS
makes use of two distances, the SRS results are plotted on
the horizontal axis using ρB . The color coding corresponds toFig. 3 Changes in tissue oxygen saturation StO2 due to position of
low oxygen tissue in the source–detector axis direction.

ρA ¼ 30 mm, ρB ¼ 35 mm. The voxel was 0.5 mm on eachside. The values of 10 voxels near the center (−2.5 < y < 2.5)
were summed to clarify the trend. The red/yellow/green plots
represent positive changes in spatial slope for a 10% increase inabsorption, and the blue/cyan plots represent negative changes.
(a)

(d)

(b)

(e)

(c)

(f)

Fig. 4 Measurement sensitivity of SRS and CWS for each model, each layer, and each detector pair.
Symbols (a)–(f) correspond to Fig. 1. Detector pair of (b) is the same as (a), and pairs of (d)–(f) are shownin (c).

Journal of Biomedical Optics

030503-3

Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 02 Dec 2019
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

March 2018 • Vol. 23(3)JBO Lettersthe color given to each layer in Fig. 1. In all the models,the CWS sensitivity for both layers and the SRS sensitivity
for the deep layer increased as the source–detector distance ρ
increased, whereas the SRS sensitivity in the surface layer
decreased. The cancelation produced by the positive and negative sensitivities reduced the sensitivity in the surface layer. In
direct contact cerebral measurement [Fig. 4(a)], the region ofinterest is often the surface layer. When ρ was <15 mm, the sensitivity of both SRS and CWS in the surface layer was greater.
As shown in Figs. 4(b)–4(f), hemodynamics in the surface layer
may affect the measured values. In the case of the small animalmodel [Fig. 4(b)], deep sensitivity was dominant, especiallywhen using SRS. This suggests that (i) cerebral tissue can be
sufficiently measured only after shaving and (ii) craniotomy is
not necessary. In the fetus or neonate model brain [Fig. 4(c)],SRS measurement with the detector pair at 4 to 6 mm showedalmost the same sensitivity in the surface and deep layers. Even
when a probe with a ρ of <10 mm was used, sensitivity in the
deep layer remained relatively strong. In the model of the adultbrain [Fig. 4(d)], it was necessary to set ρB to 28 mm or more.
The muscle measurement model used both thin [Fig. 4(e)] andthick [Fig. 4(f)] fat layers. The influence of skin was very slightwhen ρB > 20 mm. It was demonstrated that, across a wide
range of conditions, the influence of the surface layer could
be canceled when SRS measurement was used as long as thelayer was homogeneous. To cancel the changes in the surface
tissue, it is necessary to avoid applying force only near thedetectors and to avoid contact with heterogeneous parts suchas the subcutaneous veins. By comparing the SRS sensitivity
reported in this study with the CWS sensitivity reported inearlier studies,3,4,7–11 ways of suppressing or extracting theinfluence of the surface layer may be suggested.

4

Conclusions

The analysis results of the sensitivity of the voxel sensitivity andthe influence of the low- StO2 tissue position quantitatively
showed one of the factors of false negative and false positive
signals on NIRS that was discussed in the previous study.23,24
By modeling actual measurements, we also compared the difference in sensitivity of each voxel and in each layer of SRS andCWS quantitatively. Measurement points and the ratio between
the deep layer and the surface layer in measurement were clarified. Our findings on the measurement sensitivity of SRS at eachvoxel or layer can be used for hemodynamic interpretation of
the measured values from different tissues.

Disclosures
Authors have no financial interests associated with the results ofthis study.

Acknowledgments
This work was supported in part by Japan Society for
the Promotion of Science (JSPS) under Grants-in-Aid for
Scientific Research (25350525).

2. T. Hamaoka et al., “The use of muscle near-infrared spectroscopy insport, health and medical sciences: recent developments,” Philos.
Trans. R. Soc. A: Math., Phys. Eng. Sci. 369(1955), 4591–4604 (2011).
3. M. Niwayama and T. Yamakawa, “Implantable thin NIRS probe designand sensitivity distribution analysis,” Electron. Lett. 50(5), 346–348(2014).
4. N. Kanayama and M. Niwayama, “Examiner’s finger-mounted fetal
tissue oximetry,” J. Biomed. Opt. 19(6), 067008 (2014).
5. K. Mehta et al., “Spread spectrum time-resolved diffuse opticalmeasurement system for enhanced sensitivity in detecting human brain
activity,” J. Biomed. Opt. 22(4), 045005 (2017).
6. B. B. Zimmermann et al., “Frequency domain near-infrared multiwavelength imager design using high-speed, direct analog-to-digital conversion,” J. Biomed. Opt. 21(1), 016010 (2016).
7. L. M. L. Dix et al., “Comparing near-infrared spectroscopy devices
and their sensors for monitoring regional cerebral oxygen saturationin the neonate,” Pediatr. Res. 74(5), 557–563 (2013).
8. E. Okada and D. T. Delpy, “Near-infrared light propagation in an adulthead model. II. Effect of superficial tissue thickness on the sensitivity of
the near-infrared spectroscopy signal,” Appl. Opt. 42(16), 2915–2922(2003).
9. A. V. Patil et al., “Experimental investigation of NIRS spatial sensitivity,” Biomed. Opt. Express 2(6), 1478–1493 (2011).
10. G. E. Strangman, Z. Li, and Q. Zhang, “Depth sensitivity and sourcedetector separations for near infrared spectroscopy based on the Colin27
brain template,” PLoS One 8(8), e66319 (2013).
11. A. Messere and S. Roatta, “Influence of cutaneous and muscular circulation on spatially resolved versus standard Beer–Lambert near-infrared
spectroscopy,” Physiol. Rep. 1, e00179 (2013).
12. H. Dehghani et al., “Depth sensitivity and image reconstruction analysis
of dense imaging arrays for mapping brain function with diffuse optical
tomography,” Appl. Opt. 48(10), D137–D143 (2009).
13. M. J. Saikia and R. Kanhirodan, “Region-of-interest diffuse optical
tomography system,” Rev. Sci. Instrum. 87(1), 013701 (2016).
14. S. Wan, R. R. Anderson, and J. A. Parrish, “Analytical modeling for the
optical properties of the skin with in vitro and in vivo applications,”
Photochem. Photobiol. 34(4), 493–499 (1981).
15. H. R. Eggert and V. Blazek, “Optical properties of human brain tissue,
meninges, and brain tumors in the spectral range of 200 to 900 nm,”Neurosurgery 21(4), 459–464 (1987).
16. P. Vanderzee, M. Essenpreis, and D. T. Delpy, “optical-properties ofbrain-tissue,” Proc. SPIE 1888, 454–465 (1993).
17. G. Mitic et al., “Time-gated transillumination of biological tissues andtissuelike phantoms,” Appl. Opt. 33(28), 6699–6710 (1994).
18. J. L. Robertson et al., “Effect of blood in the cerebrospinal fluid on
the accuracy of cerebral oxygenation measured by near infrared spectroscopy,” Adv. Exp. Med. Biol. 812, 233–240 (2014).
19. M. Niwayama et al., “Quantitative measurement of muscle hemoglobin
oxygenation using near-infrared spectroscopy with correction for
the influence of a subcutaneous fat layer,” Rev. Sci. Instrum. 71(12),4571–4575 (2000).
20. H. M. Hori, G. Rebora, and A. Crovato, “The thickness of human scalp:
normal and bald,” J. Invest. Dermatol. 58(6), 396–399 (1972).
21. S. J. Matcher et al., “Absolute quantification methods in tissue nearinfrared spectroscopy,” Proc. SPIE 2389, 486–495 (1995).
22. S. J. Matcher et al., “Performance comparison of several publishedtissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1),54–68 (1995).
23. I. Tachtsidis and F. Scholkmann, “False positives and false negatives infunctional near-infrared spectroscopy: issues, challenges, and the way
forward,” Neurophotonics 3(3), 031405 (2016).
24. S. Greenberg, T. Shear, and G. Murphy, “Extracranial contamination ofnear-infrared spectroscopy devices,” Anesth. Analg. 124(1), 356–358(2017).

References
1. A. Villringer et al., “Near infrared spectroscopy (NIRS): a new tool tostudy hemodynamic changes during activation of brain function inhuman adults,” Neurosci. Lett. 154(1–2), 101–104 (1993).

Journal of Biomedical Optics

030503-4

Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 02 Dec 2019
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

March 2018 • Vol. 23(3)