Special Section on Laser Applications in Life Sciences

Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera

[+] Author Affiliations
Keiichiro Yoshida

Tokyo University of Agriculture & Technology, Graduate School of Bio-Application & Systems Engineering, Koganei, Tokyo 184-8588, Japan

Izumi Nishidate

Tokyo University of Agriculture & Technology, Graduate School of Bio-Application & Systems Engineering, Koganei, Tokyo 184-8588, Japan

Tomohiro Ishizuka

Tokyo University of Agriculture & Technology, Graduate School of Bio-Application & Systems Engineering, Koganei, Tokyo 184-8588, Japan

Satoko Kawauchi

National Defense Medical College Research Institute, Division of Biomedical Information Sciences, Tokorozawa, Saitama 359-8513, Japan

Shunichi Sato

National Defense Medical College Research Institute, Division of Biomedical Information Sciences, Tokorozawa, Saitama 359-8513, Japan

Manabu Sato

Yamagata University, Graduate School of Science and Engineering, Yonezawa, Yamagata 992-8510, Japan

J. Biomed. Opt. 20(5), 051026 (Jan 23, 2015). doi:10.1117/1.JBO.20.5.051026
History: Received July 25, 2014; Accepted December 29, 2014
Text Size: A A A

Open Access Open Access

Abstract.  In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability.

Evaluating the optical properties of brain tissue is important for the application of light in clinical diagnosis, surgery, and therapeutic procedures for brain diseases. The optical properties of biological tissue have been used to evaluate spatial and/or temporal changes of neuronal activity and tissue viability in the brain as intrinsic optical signals (IOSs). IOSs in the brain are believed to be caused primarily by the following processes: hemodynamic-related changes in absorption and scattering properties, changes in absorption due to redox states of cytochromes in mitochondria, changes in scattering generated by cell swelling or shrinkage caused by water movement between intracellular and extracellular compartments,1 and changes in scattering and absorption caused by chromophore contents and cell deformations. Light in the visible to near-infrared spectral range is sensitive to the absorption and scattering properties of biological tissue. The absorption and scattering properties of in vitro tissue slices can be estimated from the measured diffuse reflectance and transmittance of tissue slices2 based on several light transport models, such as the Kubelka-Munk theory,3 the diffusion approximation to the transport equation,4 the Monte Carlo method,5 and the adding-doubling method.6 Numerous spectroscopic methods have been investigated for in vivo determination of the scattering and absorption properties in living tissues, including time-resolved measurements,7 a frequency-domain method,8 and spatially resolved measurements with continuous wave (CW) light.915

Diffuse reflectance spectroscopy (DRS) based on the measurement of CW light can be simply achieved with an incandescent white light source, inexpensive optical components, and a spectrometer. DRS is one of the most promising methods for evaluating the absorption and scattering properties of in vivo brain tissue. Several approaches using a Monte Carlo simulation-based lookup table have been investigated for determining the absorption and scattering properties of biological tissue.1619 Several imaging methods based on DRS have been used to investigate cortical hemodynamics based on changes in the absorption properties of brain tissue.2024 In order to achieve rapid multispectral imaging, the use of an acousto-optical tunable filter25 and the combination of a lenslet array with narrow-band filters26 have been proposed. On the other hand, the reconstruction of multispectral images from a red green blue (RGB) image acquired by a digital RGB camera is promising as a method of rapid and cost-effective multispectral imaging. Several reconstruction techniques for multispectral images, such as the pseudo-inverse method,2730 finite-dimensional modeling,29,31 the nonlinear estimation method,32 and the Wiener estimation method (WEM),3335 have been investigated. Among these reconstruction techniques, the WEM is one of the most promising methods for practical applications because of its simplicity, cost-effectiveness, accuracy, time efficiency, and the possibility of high-resolution image acquisition.

In the present study, we investigate a simple spectral imaging of reduced scattering coefficients μs(λ) and the absorption coefficients μa(λ) of in vivo exposed brain tissues in the range of wavelengths from visible to near-infrared based on DRS using a multispectral imaging system. Multispectral diffuse reflectance images of in vivo exposed brain are estimated from an RGB image captured by a digital RGB camera. The Monte Carlo simulation-based multiple regression analysis for the estimated absorbance spectra at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) is then used to specify the concentrations of oxygenated and deoxygenated hemoglobin as the absorption parameters, and the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function as the scattering parameters. The absorption coefficient spectrum and the reduced scattering coefficient spectrum are finally reconstructed from the hemoglobin concentrations and the scattering parameters, respectively. In order to confirm the feasibility of a method by which to evaluate the absorption and scattering properties of the cerebral cortex, we performed in vivo experiments using exposed rat brain while changing the fraction of inspired oxygen (FiO2).

Estimation of Spectral Diffuse Reflectance Images by Wiener Estimation

The response of a digital color camera in spatial coordinates (x,y) with the i’th (i=1, 2, 3) color channel, or red, green, and blue can be calculated as Display Formula

vi(x,y)=ui(λ)E(λ)S(λ)r(x,y;λ)dλ,(1)
where λ is the wavelength, ui(λ) is the transmittance spectrum of the i’th filter, E(λ) is the spectrum of the illuminant, S(λ) is the sensitivity of the camera, and r (x, y; λ) is the reflectance spectrum in the spatial coordinates (x,y). For convenience, Eq. (1) is expressed in discrete vector notation as Display Formula
v=Fr,(2)
where v is a vector having a three-element column and r is a vector having a k-element column, which corresponds to the reflectance spectrum of a pixel of an image. Moreover, F is a 3×k matrix and is expressed as Display Formula
F=UES,(3)
where U=[u1,u2,u3]T. Column vector ui denotes the transmittance spectrum of the i’th filter, and []T represents the transposition of a vector. Also, E and S are k×k diagonal matrices representing the spectrum of the illuminant and the sensitivity of the camera, respectively. The Wiener estimation3335 of r is given by Display Formula
r˜=Wv,(4)
where W is the Wiener estimation matrix. The purpose of W is to minimize the minimum square error between the original and estimated reflectance spectra. In this case, the minimum square error is expressed as Display Formula
e=(rr˜)t(rr˜).(5)

From Eqs. (4) and (5), the minimum square error is rewritten as Display Formula

e=(rr˜)t(rr˜)=rtrWrtvWtvtr+WtWvtv.(6)

The minimization of the minimum square error requires that the partial derivative of e with respect to W be zero, i.e., Display Formula

eW=rtv+Wtvtv=0.(7)

From Eq. (7), the matrix W is derived as Display Formula

W=rvTvvT1=rvTFT(FrrTFT)1,(8)
where is an ensemble-averaging operator. The derivation of matrix W requires the autocorrelation matrix rrT. In the present study, we determine rrT based on 720 different reflectance spectra obtained from in vivo rat brain under various physiological conditions.

To test the accuracy in spectral reconstruction, the estimated spectrum at 27 different wavelengths by WEM was compared with the measured spectrum by spectrometer using a goodness-of-fit coefficient (GFC).36 The GFC is based on the inequality of Schwartz and it is described as Display Formula

GFC=|jrmes(λj)rest(λj)||j[rmes(λj)]2||j[rest(λj)]2|,(9)
where rmeas(λi) is the measured original spectral data at the wavelength λI and rest(λi) is the estimated spectral data at the wavelength λi. Hernández-Andrés et al.36 suggested that colorimetrically accurate rmeas(λi) requires a GFC>0.995; a “good” spectral fit requires a GFC0.999, and GFC0.9999 is necessary for an “excellent” spectral fit.

Estimation of the Absorption Coefficient and the Reduced Scattering Coefficient

Figure 1 shows a flow diagram of the method used to estimate the absorption coefficient μa(λ) and the reduced scattering coefficient μs(λ). The absorbance spectrum A(λ) is defined as Display Formula

A(λ)=log10r(λ),(10)
where r(λ) is the diffuse reflectance spectrum normalized by the incident light spectrum. Since attenuation due to light scattering can be treated as a pseudochromophore, the absorbance spectrum A(λ) can be approximated as the sum of attenuations due to absorption and scattering in the brain as Display Formula
A(λ)=CHbOl(λ,CHbO,CHbR,μs)εHbO(λ)+CHbRl(λ,CHbO,CHbR,μs)εHbR(λ)+D(λ,μs),(11)
where C is the concentration, l is the mean path length, ε(λ) is the extinction coefficient, and D(λ,μs) indicates attenuation due to light scattering in the tissue. The subscripts HbO and HbR denote oxygenated hemoglobin and deoxygenated hemoglobin, respectively. The absorption coefficient of the cortical tissue was assumed to depend only on the concentrations of HbO and HbR (CHbO, CHbR) as Display Formula
μa(λ)=Cε(λ)=CHbOεHbO(λ)+CHbRεHbR(λ).(12)

Graphic Jump LocationF1 :

Flow diagram of the process for estimating the concentration of oxygenated hemoglobin CHbO, the concentration of deoxygenated hemoglobin CHbR, coefficient a, exponent b, absorption coefficient μa(λ), and reduced scattering coefficient μs(λ). (a) Preparation work for determining the regression equations and (b) main process for deriving μa(λ) and μs(λ) from the reflectance spectrum r(λ) estimated by the Wiener estimation method (WEM).

The total hemoglobin concentration CHbT is defined as the sum of CHbO and CHbR as follows: Display Formula

CHbT=CHbO+CHbR.(13)

The tissue oxygen saturation is determined as Display Formula

StO2[%]=100×CHbOCHbT.(14)

The reduced scattering coefficient of the brain tissue was assumed to take the following form as a power law function:37,38Display Formula

μs(λ)=aλb,(15)
where the coefficient a and the exponent b represent the scattering amplitude and the scattering power, respectively. Using A(λ) as the response variable and ε(λ) as the predictor variables, the multiple regression analysis based on the modified Lambert-Beer law (MRA1) can be applied to Eq. (11) as Display Formula
A(λ)=αHbOεHbO(λ)+αHbRεHbR(λ)+α0,(16)
where αHbO, αHbR, and α0 are the regression coefficients. The regression coefficients αHbO and αHbR describe the degree of contribution of each extinction coefficient to A(λ) and are closely related to the concentrations CHbO and CHbR, respectively. The regression coefficient α0 is expressed as Display Formula
α0=A¯ε¯HbOαHbOε¯HbRαHbR,(17)
where A¯, ε¯HbO, and ε¯HbR are the averages of A(λ), εHbO(λ), and εHbR(λ), respectively, over the wavelength range, and α0 represents the bias component of A(λ). Thus, a0 describes the degree of contribution of the attenuation due to light scattering in the brain to the absorbance spectrum A(λ) and so is related to the coefficient a and the exponent b in Eq. (15). At the same time, α0 is also affected by the absorption coefficient of the brain, since A(λ) is generally a function of the tissue absorption coefficient and the reduced scattering coefficient.

In order to investigate the relationship between the regression coefficients and the values of CHbO, CHbR, a, and b, we performed MCS for the diffuse reflectance from the rat cortical tissue through the skull at λ=500, 520, 540, 560, 570, 580, 600, 730, and 760 nm using various values of CHbO, CHbR, a, and b. We used the MCS source code developed by Wang et al.,5 in which the Henyey-Greenstein phase function is applied to the sampling of the scattering angle of photons. The simulation model consisted of a single layer representing cortical tissue. In a single simulation of diffuse reflectance at each wavelength, 5,000,000 photons were randomly launched. The absorption coefficients of oxygenated hemoglobin μa,HbO(λ) and deoxygenated hemoglobin μa,HbR(λ) were obtained from the values of εHbO(λ) and εHbR(λ) in the literature,39 where the hemoglobin concentration of blood with a 44% hematocrit is 2326μM of hemoglobin. For the reduced scattering coefficients, the values of a were 60,258, 80,344, 100,430, 120,516, and 180,774 in the simulation, which were derived by multiplying the typical value40 of a by 0.5, 0.75, 1.0, 1.25, and 1.5, respectively. The values of b were 1.2442, 1.31332, 1.3824, 1.45156, and 1.52068, which were derived by multiplying the typical value40 of b by 0.5, 0.75, 1.0, 1.25, and 1.5, respectively. The reduced scattering coefficients μs(λ) of the cortical tissue were obtained from Eq. (15). The sum of the absorption coefficients of oxyhemoglobin and deoxyhemoglobin, μa,HbO(λ) and μa,HbR(λ), represents the absorption coefficient of total hemoglobin μa,HbT(λ). The values for CHbT=4.652, 23.26, and 116.3μM were used as input to the cortical tissue in the MCS. Tissue oxygen saturation was assumed to be StO2=60% for all combinations. For all simulations, the refractive index of the cortical tissue nc was fixed at 1.4. The thickness of the cortical tissue in each case was set to 5.0 mm.

Figures 2(a) and 2(b) show the values of αHbO and αHbR versus the volume concentrations of oxyhemoglobin CHbO and deoxyhemoglobin CHbR, respectively, for various values of a, as obtained from the MCS. Figures 2(c) and 2(d) show the values of αHbO and αHbR versus the volume concentrations of oxyhemoglobin CHbO and deoxyhemoglobin CHbR, respectively, for various values of b, as obtained from the MCS. In both Figs. 2(a) and 2(c), the value of αHbO increases with the increase of CHbO. Moreover, the value of αHbO changes with the increase in the values of a and b. The same tendency can be seen for αHbR, as shown in Figs. 2(b) and 2(d). Figures 2(e) and 2(f) show the values of α0 versus the values of a and b, respectively, for various values of CHbO and CHbR. The value of α0 decreases with the increase in the value of a. Moreover, the value of α0 increases with the increases in CHbO and CHbR. On the other hand, the value of α0 increases with the increase in the value of b. Therefore, the regression coefficients αHbO, αHbR, and α0 are related to the volume concentration of oxyhemoglobin CHbO, that of deoxyhemoglobin CHbR, the coefficient a, and the exponent b, respectively. However, CHbO, CHbR, a, and b are not determined by a unique regression coefficient when using only MRA1.

Graphic Jump LocationF2 :

Regression coefficients versus concentrations of oxygenated hemoglobin CHbO and deoxygenated hemoglobin CHbO, coefficient a, and exponent b, obtained from the Monte Carlo simulations. (a) Regression coefficient αHbO versus concentration CHbO for various values of a. (b) Regression coefficient αHbR versus concentration CHbR for various values of a. (c) Regression coefficient αHbR versus concentration CHbO for various values of b. (d) Regression coefficient αHbR versus concentration CHbR for various values of b. (e) Regression coefficient α0 versus coefficient a for various values of CHbO and CHbR. (f) Regression coefficient α0 versus exponent b for various values of CHbO and CHbR.

Thus, we conducted further multiple regression analyses to estimate the values of CHbO, CHbR, a, and b based on the combination of regression coefficients αHbO, αHbR, and α0 that were obtained from MRA1. In this analysis, CHbO, CHbR, and b were regarded as response variables, and the three regression coefficients αHbO, αHbR, and α0 in Eq. (16) were regarded as predictor variables to determine the regression equations for CHbO, CHbR, and b. The regression equations for CHbO, CHbR, and b are written as Display Formula

CHbO=βHbO·α1,(18)
Display Formula
CHbR=βHbR·α1,(19)
Display Formula
b=βb·α1,(20)
Display Formula
α1=[1,αHbO,αHbR,α0]T,(21)
Display Formula
βHbO=[βHbO,0,βHbO,1,βHbO,2,βHbO,3],(22)
Display Formula
βHbR=[βHbR,0,βHbR,1,βHbR,2,βHbR,3],(23)
Display Formula
βb=[βb,0,βb,1,βb,2,βb,3].(24)

The symbol []T represents the transposition of a vector. We refer to this analysis as MRA2. On the other hand, in the preliminary investigation, we found that adding b to the predictor variables can improve the accuracy of the estimation of a. Therefore, b, αHbO, αHbR, and α0 are regarded as the predictor variables, and the given values of a are regarded as the response variables for determining the regression equation for a, which is written as Display Formula

a=βa·α2,(25)
where Display Formula
α2=[1,αHbO,αHbR,α0,b]T,(26)
Display Formula
βa=[βa,0,βa,1,βa,2,βa,3βa,4].(27)

We refer to this analysis as MRA3. The coefficients βHbO,j, βHbR,j, βb,j, (j=0, 1, 2, 3), and βa,k (k=0, 1, 2, 3, 4) are unknown and must be determined before estimating CHbO, CHbR, b, and a.

We used MCS as the foundation to determine reliable values of βHbO,j, βHbR,j, βb,j, and βa,k. The simulation model used here consisted of a single layer of cortical tissue, in which μa(λ) and μs(λ) are homogeneously distributed. The absorption coefficients μa(λ) converted from the concentrations CHbO and CHbR and the reduced scattering coefficient μs(λ) deduced by the coefficient a and the exponent b were provided as inputs to the simulation, whereas the diffuse reflectance spectrum r(λ) was derived as output. The input values of CHbO, CHbR, a, and b and the output reflectance spectra are useful as the data set in statistically determining the values of βHbO,i, βHbR,i, βb,i, and βa,j for determining the absolute values of CHbO, CHbR, a, and b. The five different values of 60,258, 80,344, 100,430, 120,516, and 180,774 were calculated by multiplying the typical value40 of a by 0.5, 0.75, 1.0, 1.25, and 1.5, respectively, whereas the five values of 1.2442, 1.31332, 1.3824, 1.45156, and 1.52068 were calculated by multiplying the typical value40 of b by 0.5, 0.75, 1.0, 1.25, and 1.5, respectively. The reduced scattering coefficients μs(λ) of the cortical tissue with the 25 different values were derived using Eq. (15). The sum of the absorption coefficients of oxyhemoglobin and deoxyhemoglobin μa,HbO(λ)+μa,HbR(λ)=μa,HbT(λ) for CHbT=4.652, 23.26, and 116.3μM was used as input to the cortical tissue in the simulation. Tissue oxygen saturation StO2 was determined by μa,HbO(λ)/μa,HbT(λ), and values of 0%, 20%, 40%, 60%, 80%, and 100% were used for the simulation. The above values were also used for the refractive index of the cortical layer. In total, 450 diffuse-reflectance spectra at λ=500, 520, 540, 560, 570, 580, 600, 730, and 760 nm were simulated under the various combinations of CHbO, CHbR, a, and b. The MRA1 analysis for each simulated spectrum based on Eq. (16) generated the 450 sets of vector α1 and concentrations CHbO and CHbR and exponent b, and the 450 sets of vector α2 and coefficient a. The coefficient vectors βHbO, βHbR, and βb were statistically determined by performing MRA2, whereas the coefficient vector βHbO was determined statistically by performing MRA3. Once βHbO, βHbR, βb, and βa were obtained, CHbO, CHbR, a, and b were calculated from αHbO, αHbR, and α0, which were derived from MRA1 for the measured reflectance spectrum, without the MCS, as shown in Fig. 1(b). Therefore, the spectrum of the reduced scattering coefficient μs(λ) and that of the absorption coefficient μa(λ) were reconstructed by Eqs. (12) and (15), respectively, from the measured reflectance spectrum.

Imaging System

Figure 3(a) shows a schematic diagram of the experimental system used in the present study. A white-light emitting diode (LED) (LA-HDF158A, Hayashi Watch Works Co., Ltd., Tokyo, Japan) illuminated the surface of the exposed cortex via a light guide and a ring-shaped illuminator with a polarizer. The light source covered a range from 400 to 780 nm. Diffusely reflected light was received by a 24-bit RGB CCD camera (DFK-31BF03.H, Imaging Source LLC, Charlotte, NC, USA) without an IR cut filter via an analyzer and a camera lens to acquire an RGB image of 640×480pixels. The primary polarization plate (ring-shaped polarizer) and the secondary polarization plate (analyzer) were placed in a crossed Nicols alignment in order to reduce specular reflection from the sample surface. A standard white diffuser was used to regulate the white balance of the camera. In order to evaluate the accuracy of the WEM, the reflectance spectra of cortex were simultaneously measured by a fiber-coupled spectrometer (USB4000-XRS-ES, Ocean Optics Inc., Dunedin, Florida, USA) for an integration time of 130 ms as reference data. Before the measurements of RGB images and reflectance spectra, the area measured using the spectrometer was confirmed by projecting light from a halogen lamp (HL-2000, Ocean Optics Inc.) onto the surface of the cortex via one lead of a bifurcated fiber, a lens, and a beam splitter. The RGB image of the sample, including the spot of light illuminated by the halogen lamp, was stored in the PC, and the size and coordinates of the spot were then specified as the area measured by the spectrometer. After the halogen lamp was turned off, the measurements of RGB images and reflectance spectra were performed simultaneously. Using the WEM, reflectance images ranging from 500 to 760 nm at intervals of 10 nm were reconstructed from an RGB image acquired at an exposure time of 65 ms. This means that the spectral reflectance images at 27 wavelengths can be obtained with a temporal resolution of 15 fps. The field of view of the system was 9.31×6.98mm2 with 1024×768pixels. The lateral resolution of the images was estimated to be 9.1μm. The multispectral reflectance images with 400×400pixels at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) were then used to estimate the images of CHbO, CHbR, a, b, μa(λ), and μs(λ) according to the process described in Fig. 1.

Graphic Jump LocationF3 :

(a) Schematic diagram of the experimental system and (b) representation of the rat skull with an RGB image of the exposed rat brain.

Phantom Experiments

To confirm the validity of the proposed method, we performed experiments using tissue-like optical phantoms. We prepared agar solution by diluting agarose powder (Fast Gene AG01; NIPPON Genetics EUROPE GmbH, Düren, NRW, Germany) with saline at a weight ratio of 1.0%. To simulate the scattering condition, a mixture of polystyrene latex beads solution with a 0.1-μm mean particle size (LB1-15L; Sigma-Aldrich Japan K. K. Tokyo, Japan) and that with a 1.1-μm mean particle size (LB11-15L; Sigma-Aldrich Japan K. K.) was added to the agarose solution. The resultant solution was used as the base material. The volume concentration of the polystyrene solutions ranged from 2.75% to 11.0%. An optical phantom layer was made by adding a small amount of fully oxygenated hemoglobin extracted from horse blood to the base material. All phantoms were hardened in molds having the required thickness and size by being cooled at approximately 5.5°C for 30 min. The thickness of each phantom was 0.5 cm, while the area of each phantom was 2.6×4.5cm2. In the preliminary experiments, we attempted to measure the phantoms with hemoglobin deoxygenated by Na2S2O4 solution. However, the measured diffuse reflectance spectra had a tendency to exhibit unexpected fluctuation and it was difficult to maintain the stable condition of the deoxygenated spectra during the experiments. To avoid the potential uncertainty of the estimation of the total hemoglobin with the condition of StO2=0%, the phantoms with hemoglobin deoxygenated by Na2S2O4 solution were excluded from the measurements in this study. We made 22 optical phantoms with different combinations of CHbO, CHbR, a, and b.

As preparatory measurements, we first determined the absorption coefficient μa and reduced scattering coefficients μs of each phantom at 500, 520, 540, 560, 570, 580, 600, 730, and 760 nm, because they are necessary for the MCS to deduce the empirical formula for CHbO, CHbR, a, and b. The spectra of the absorption coefficient μa(λ) and reduced scattering coefficients μs(λ) are also required to calculate the given values for CHbO, CHbR, a, and b. For this purpose, we measured the diffuse reflectance and total transmittance spectra of each phantom individually. A 150-W halogen-lamp light source (LA-150SAE; Hayashi Watch Works Co., Ltd.) illuminated the phantom via a light guide (LGC1-5L1000; Hayashi Watch Works Co., Ltd.) and lens with a spot diameter of 0.2 cm. The diameter and focal length of the lens are 5.0 and 10 cm, respectively. The thickness of each phantom in the preparatory measurements was 0.1 cm, while the area of each phantom was 2.6×4.5cm2. The phantom was placed between two glass slides having a thickness of 0.1 cm and fixed at the sample holder of an integrating sphere (RT-060-SF, Labsphere Incorporated). The detected area of the phantom was circular with a diameter of 2.2 cm. Light diffusely reflected from the detected area was received at the input face of an optical fiber probe having a diameter of 400μm which was placed at the detector port of the sphere. The fiber transmits the received light into a multichannel spectrometer (USB2000, Ocean Optics Inc.), which measured reflectance or transmittance spectra in the visible to near-infrared wavelength region under the control of a personal computer.

To determine μa(λ) and μs(λ) from the measured diffuse reflectance and total transmittance spectra, we utilized the inverse Monte Carlo method (IMC).41 In the IMC, the MCS of the reflectance and transmittance spectra were iterated for different values of μa(λ) and μs(λ) until the difference between the simulated and measured spectral values decreased below a predetermined threshold. The values used in the last step of the iteration were adopted as the final results. This process was carried out at 500, 520, 540, 560, 570, 580, 600, 730, and 760 nm, and wavelength-dependent properties of μa(λ) and μs(λ) were obtained for each phantom. In these calculations, the refractive index was assumed to be 1.33 for all phantoms in the whole wavelength range. The concentrations of oxygenated hemoglobin CHbO and deoxygenated hemoglobin CHbR in each phantom were calculated from Eq. (12) with the estimated absorption coefficient μa(λ) and the known values of εHbO(λ) and εHbR(λ). The concentration of total hemoglobin CHbT and the tissue oxygen saturation StO2 in each phantom was calculated from Eqs. (13) and (14), respectively. The coefficient a and the exponent b for each phantom were calculated from the estimated μs(λ) based on Eq. (15). Those values of μa, CHbO, CHbR, CHbT, StO2, μs, a, and b obtained by IMC are used as the given values to evaluate the validity of the proposed method experimentally.

We also need to have the conversion vectors for the phantoms used in this study. We generated diffuse reflectance spectra at 500, 520, 540, 560, 570, 580, 600, 730, and 760 nm using the MCS with the conditions of the phantoms. For this simulation, the values of μa and μs at 500, 520, 540, 560, 570, 580, 600, 730, and 760 nm were set to be the same as those estimated by IMC. Performing MRA1, MRA2, and MRA3 as described in Sec. 2, we derived the conversion vectors and the corresponding empirical formula for estimating the volume concentrations of oxygenated hemoglobin CHbO, that of deoxygenated hemoglobin CHbR, the coefficient a, and the exponent b.

Animal Experiments

Animal care and experimental procedures were approved by the Animal Research Committee of Tokyo University of Agriculture and Technology. Intraperitoneal anesthesia was implemented with α-chloralose (50mg/kg) and urethane (600mg/kg) in five adult male Wister rats (134 to 426 g). Anesthesia was maintained at a depth such that the rat had no response to toe pinch. The rat head was placed in a stereotaxic frame. A longitudinal incision of 20mm in length was made along the head midline. The skull bone overlying the parietal cortex was removed with a high-speed drill to form an ellipsoidal cranial window, as shown in Fig. 3(b). In order to confirm the change in reflectance spectra of exposed rat brain, we performed the measurements during normoxia (t=0 to 5 min), hyperoxia (t=5 to 10 min), and anoxia (t=10 to 30 min) by varying FiO2. Hyperoxia (FiO2=95%) was induced by 95%O25%CO2 gas inhalation, for which a breath mask was used under spontaneous respiration, whereas anoxia (FiO2=0%) was induced by 95%N25%CO2 gas inhalation. In order to identify the respiration arrest (RA), the respiration of the rat was confirmed by observing the periodical movement of the lateral region of the abdomen during the experiments.

In order to evaluate the magnitude of signal S induced by hyperoxia and anoxia, we calculated the change in the signal based on the time series data. The signal at the onset of measurement was selected as a control Sc, which was subtracted from each of the subsequent signals S in the series. Each subtracted value, which demonstrated the change in the signal, SSc, over time, was normalized by dividing by Sc. The change in the signal is expressed as ΔS={(SSc)/Sc}×100. The above calculation was applied to the time series of CHbO, CHbR, CHbT, StO2, a, b, and μs(λ). The time of RA can differ from sample to sample. Therefore, we divided the time course of the estimated value during anoxia into two periods: anoxia 1 and anoxia 2. Anoxia 1 is the period between the onset of anoxia and RA, whereas anoxia 2 is the period between RA and the end of the measurement. The time averages of the estimated values over the periods of normoxia, hyperoxia, anoxia 1, and anoxia 2 were then calculated and averaged over all five samples.

Statistical Considerations

A region of interest (ROI) of 40×40pixels was placed in an image for each resultant image. An unpaired Student’s t-test was used for statistical analysis when comparing the in vivo results of the normoxic condition with those for the hyperoxic condition or with those for the anoxic conditions. The normality of the averaged value over the seven samples for each condition was tested using a Shapiro-Wilk test before the Student’s t-test. A P value of <0.05 was considered to be statistically significant.

Figure 4 shows the comparisons between the estimated and given values for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b, obtained from the phantom experiments. In Figs. 4(a), 4(c), 4(e), and 4(f), the estimated values are well correlated with the given values. Correlation coefficients between the estimated and given values are 0.91 (P<0.0001), 0.95 (P<0.0001), 0.84 (P<0.0001) and 0.46 (P=0.029) for CHbO, CHbT, a, and b, respectively. In Figs. 4(b) and 4(d), all estimated values of CHbR and StO2 are close to 0μM and 100%, respectively, which is consistent with the fact that the hemoglobin solution used in the phantoms was fully oxygenated hemoglobin, as described above. These results indicate the validity of the proposed method for estimating the volume concentrations CHbO and CHbR, the coefficient a, and the exponent b. Figure 5 shows the comparisons between the estimated and given values for (a) absorption coefficient μa and (b) reduced scattering coefficient μs. Reasonable results were obtained for both μa and μs. Correlation coefficients between the estimated and given values are 0.97 (P<0.0001) and 0.78 (P<0.0001) for μa and μs, respectively. These results demonstrate the validity of the proposed method for estimating absorption and reduced scattering coefficients from the diffuse reflectance spectrum.

Graphic Jump LocationF4 :

Comparisons between the estimated and given values for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b, obtained from the phantom experiments.

Graphic Jump LocationF5 :

Comparisons between the estimated and given values for (a) absorption coefficient μa and (b) reduced scattering coefficient μs, obtained from the phantom experiments.

Figure 6 shows the typical spectral reflectance images at 500, 520, 540, 560, 570, 580, 600, 730, and 760 nm estimated from the RGB image of in vivo rat brain under normoxia by the WEM. Spectral reflectance images of the cerebral cortex were successfully reconstructed from the RGB image using the proposed method. The distribution of blood vessels in the cortical tissue can be clearly recognized in the estimated reflectance images at 500, 520, 540, 560, 570, and 580 nm. The images at 600, 730, and 760 nm have low contrast between the parenchyma region and the blood vessel region, which indicates the lower absorption coefficient of hemoglobin at lower wavelengths.

Graphic Jump LocationF6 :

Typical estimated multispectral images r(λ) of the exposed rat brain under normoxia.

Figure 7 shows the reflectance spectra estimated using the WEM and the reflectance spectra measured using the spectrometer for (a) normoxia, (b) hyperoxia, and (c) anoxia immediately after RA. The estimated reflectance spectra for a blood vessel and that for parenchyma in each graph of Fig. 7 is the average values over the ROIb and ROIp, respectively. In this case, both ROIb and ROIp were selected to be the same as the measured area by the spectrometer. The reflectance spectra estimated using the WEM are comparable to the spectra measured using the spectrometer for the different respiration conditions. The estimated spectra under hyperoxia and anoxia are dominated by the spectral characteristics of oxygenated hemoglobin and deoxygenated hemoglobin, respectively. The values of GFC obtained from five samples summarized in Table 1 in the revised manuscript indicate the accurate spectral reconstruction by WEM.

Graphic Jump LocationF7 :

Comparison of the estimated reflectance spectra averaged over the ROIs in Fig. 4 (white squares) and the reference spectra measured by spectrometer for (a) normoxia, (b) hyperoxia, and (c) anoxia.

Table Grahic Jump Location
Table 1The goodness-of-fit coefficient (GFC) obtained from the five samples.

Figure 8 shows the typical estimated images of exposed rat brain under normoxia obtained using the proposed method for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b. In Figs. 8(a)8(c), the values of CHbO, CHbR, and CHbT, respectively, in the blood vessel region are higher than those in the parenchyma region, which indicates the difference in blood volume between the blood vessels and the parenchyma. The distribution of StO2 in the blood vessel region in Fig. 8(d) is probably due to the difference between artery and vein. The average values over the entire region were calculated to be CHbO=150.08±50.61μM, CHbR=40.11±12.12μM, CHbT=190.19±52.05μM, StO2=75.52±7.62%, a=134,458±3,634, and b=1.33±0.02, whereas those over the ROI on parenchyma (white square in each image of Fig. 8) were calculated to be CHbO=41.16±3.13μM, CHbR=42.34±5.51μM, CHbT=83.49±6.52μM, StO2=49.29±4.19%, a=136,811±2298, and b=1.32±0.01.

Graphic Jump LocationF8 :

Estimated images of exposed rat brain obtained using the proposed method under normoxia for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b.

Figure 9 shows the reconstructed images of the exposed rat brain for (a) the absorption coefficient spectrum μa(λ) and (b) the reduced scattering coefficient spectrum μs(λ) under normoxia. The average values of μa(λ) and μs(λ) over the ROIs [white squares in Figs. 9(a) and 9(b)] are also shown in Fig. 10. The error bars mean the standard deviations for all pixels in the ROI. The wavelength dependence of μa(λ) is dominated by the spectral characteristics of hemoglobin.39 The reduced scattering coefficients μs(λ) have a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths. The spectral features of μs correspond to the typical spectrum of brain tissue published in the literature.40

Graphic Jump LocationF9 :

Typical estimated images of exposed rat brain obtained using the proposed method under normoxia for (a) absorption coefficient μa(λ) and (b) reduced scattering coefficient μs(λ).

Graphic Jump LocationF10 :

Estimated spectra averaged over the ROI (white square in Fig. 9) for (a) absorption coefficient μa(λ) and (b) reduced scattering coefficient μs(λ). The error bars mean the standard deviations for all pixels in the ROI.

Figure 11 shows the comparison between the measured reflectance spectrum and the predicted reflectance spectrum for the μa and μs shown in Fig. 10. The predicted reflectance spectrum is comparable to the measured spectrum. The value of GFC in this case was 0.9974, which shows a good agreement between the measured reflectance spectrum and the predicted reflectance spectrum.

Graphic Jump LocationF11 :

Comparison between the measured reflectance spectrum and the predicted reflectance spectrum for the μa and μs, as shown in Fig. 10.

Figure 12 shows the typical in vivo results for CHbO, CHbR, CHbT, StO2, a, and b while varying FiO2. Figure 13 shows the time courses of change in CHbO, CHbR, CHbT, and StO2 averaged over the area for the ROI in the parenchyma, as shown in Fig. 12, while varying FiO2. The values of CHbO and CHbR were increased and decreased, respectively, during hyperoxia, which caused the increase in StO2. After the onset of anoxia, the values of CHbO and CHbR decreased and increased, respectively. Consequently, the value of StO2 was dramatically decreased. The value of CHbT begins to increase before RA and reaches a maximum amplitude approximately 1 min after RA, which is indicative of an increase in blood flow for compensating hypoxia. The period between the onset of anoxia and respiratory arrest averaged over all five samples was 499±207s. Immediately after RA, the values of both CHbO and CHbT decreased rapidly. The time courses of CHbO, CHbR, CHbT, and StO2 while varying FiO2 are consistent with the well-known hemodynamic responses to the change in the fraction of inspired oxygen. Minor fluctuations in both CHbO and CHbT approximately 2.5 min after RA are probably caused by the changes in blood volume due to vasoconstriction and vasodilatation.

Graphic Jump LocationF12 :

Typical images for in vivo results while varying FiO2 (from top to bottom: RGB image, CHbO, CHbR, CHbT, StO2, a, and b).

Graphic Jump LocationF13 :

Time courses of ΔCHbO, ΔCHbR, ΔCHbT, and ΔStO2 averaged over the ROI in the parenchyma region shown in Fig. 9 while varying FiO2.

Figure 14 shows the time averages over the period of normixia, hyperoxia, anoxia 1 (before RA), and anoxia 2 (after RA) after respiratory arrest for (a) CHbO, (b) CHbR, (c) CHbT, and (d) StO2 averaged over the ROIs for all five samples. The trends of CHbO, CHbR, CHbT, and StO2 shown in Figs. 12 and 13 were apparent in all five samples. The average values of StO2 during normoxia over all five samples were 47.49±14.42%, which is lower than the value of around 60% reported in the literature.38,42 This may be due to the experimental conditions of the present study, such as the type of anesthetic used, the anesthetic depth, or uncontrolled body temperature.

Graphic Jump LocationF14 :

Time averages over the period of normoxia, hyperoxia, anoxia 1 (before RA), and anoxia 2 (after RA) after respiratory arrest for (a) ΔCHbO, (b) ΔCHbR, (c) ΔCHbT, and (d) ΔStO2 averaged over the ROIs for all five samples. The error bars show the standard deviations (n=5). *P<0.05.

Figure 15 shows the time courses of coefficient a, exponent b, μs(500), and μs(760) averaged over the area corresponding to the ROIs in the parenchyma, as shown in Fig. 12, while varying FiO2. During the period from the onset of anoxia until RA, coefficient a and exponent b decrease slightly and increase slightly, respectively. The changes in the scattering parameters decrease the reduced scattering coefficient μs. On the other hand, the values of μs(500) and μs(760) increased remarkably after RA.

Graphic Jump LocationF15 :

Time courses of Δa, Δb, Δμs(500), and Δμs(760) averaged over the ROI in the parenchyma region shown in Fig. 9 while varying FiO2.

Figure 16 shows the time average over the period of normixia, hyperoxia, anoxia 1, and anoxia 2 for (a) coefficient a, (b) exponent b (c) μs(500), and (d) μs(760) averaged over the ROIs for all five samples. The same tendencies of a, b, μs(500), and μs(760) shown in Figs. 12 and 15 were applicable to the average values over all five samples. Changes in the reduced scattering coefficients after the onset of N2-inhalation imply morphological changes in brain tissue. Mitochondrial respiration is inhibited during the ischemia-like condition by the rapid drop in O2 tension, which causes depletion of adenosine triphosphate (ATP). Reduction of ATP production due to the inhibition of mitochondrial respiration leads to failure of the Na+/K+ATPase pump.43 In such a case, extracellular Na+, Cl, and Ca2+ rush in, with water following osmotically, causing cell swelling.44,45 Thus, the slight decreases in μs in Figs. 12, 15, and 16 are most likely caused by cell swelling due to failure of the Na+/K+ATPase pump. The remarkable increases in μs after RA are probably caused by the dendritic beading effect, which is indicative of neuronal damage.44 The necklace-like structure of a large amount of dendritic processes is highly efficient at scattering light, such that bead formation over several minutes reduces the transmitted light intensity.4648

Graphic Jump LocationF16 :

Time averages over the period of normoxia, hyperoxia, anoxia 1 (before RA), and anoxia 2 (after RA) after respiratory arrest for (a) Δa, (b) Δb, (c) Δμs(500), and (d) Δμs(760) averaged over the ROIs for all five samples. The error bars show the standard deviations (n=5). *P<0.05.

Since the method relying on diffusing reflection integrates all information along the depth direction, the method does not have depth resolution. The depth and diameter of blood vessels usually differ among samples and may change due to the age of the rat. Their correct estimation is essential for precisely estimating the concentrations of oxygenated hemoglobin and deoxygenated hemoglobin for the blood vessel regions. In the present study, we assumed the scattering spectrum of the in vivo brain tissue as a power law function. This assumption may be applicable to the parenchyma region with a low volume concentration of blood. However, the diffuse reflectance spectrum from the blood vessel region with a higher volume concentration of blood can be strongly influenced by the scattering properties of the red blood cells (RBCs). It has been reported that RBCs behave as strong scatterers of light, and the reduced scattering coefficient spectrum has a similar wavelength dependence to the absorption spectrum of hemoglobin.49 Using the empirical formulas obtained from the MCS with an inhomogeneous layer model in which the reduced scattering coefficient spectrum is considered may enable more accurate estimations of the reduced scattering coefficients for the blood vessel regions.

It has been reported that the use of the Lambert-Beer approximation in the analysis of data for ischemia condition in which the values of CHbO and CHbR are dramatically changed provides serious errors.20 The most serious problem is a large overestimation of the reduction in CHbO, leading to calculated values of CHbO of less than zero. For this problem, they used a nonlinear minimization method to improve the accuracy of the estimations for CHbO and CHbR. We also analyzed the absorbance spectrum based on the Lambert-Beer law for estimating CHbO and CHbR. In this case, the absorbance spectrum was linearly related to CHbO and CHbR. In addition, we used a linear regression model to specify the relationship between the sets of regression coefficients αHbO, αHbR, and α0 obtained from MRA1 and the values of CHbO, CHbR, a, and b. Therefore, the results obtained by the proposed method may also have some errors in CHbO and CHbR due to the linearization. Although the phantom experiments showed reasonable results for CHbO, CHbR, CHbT, and StO2, the estimated values of a and b have relative large deviations from the given values, as shown in Fig. 4. Those deviations account for the estimation error in μs shown in Fig. 5(b). The use of αHbO, αHbR, and α0 and their higher order terms for the vector α1 and α2 can extend the current linear empirical formula to the nonlinear regression models. It may be useful to improve the accuracy of the estimations for CHbO, CHbR, a, and b. These issues should be investigated in the future.

In summary, a method for imaging reduced scattering coefficients μs(λ) and the absorption coefficients μa(λ) of in vivo exposed brain tissues based on spectral reflectance images reconstructed from a single snap shot of an RGB image using the WEM was demonstrated in the present report. In vivo experiments using exposed rat brain while changing the fraction of inspired oxygen confirmed the feasibility of the proposed method for evaluating both cortical hemodynamics and changes in tissue morphologies due to loss of tissue viability in the brain.

Since the proposed method visualizes both the hemodynamic response and the morphological changes in brain tissue, it may be useful for evaluating brain function and tissue viability in neurosurgery as well as in the diagnosis of several neurological disorders, such as neurotrauma, seizure, stroke, and ischemia. We intend to extend the proposed method in order to investigate brain function in cortical spreading depression and anoxic depolarization.

The present study was supported in part by a Grant-in-Aid for Scientific Research from the Japanese Society for the Promotion of Science.

Bonhoeffer  T., Grinvald  A., Brain Mapping: The Methods. ,  Academic Press ,  San Diego  (1996).
Nishidate  I., Yoshida  K., Sato  M., “Changes in optical properties of rat cerebral cortical slices during oxygen glucose deprivation,” Appl. Opt.. 49, (34 ), 6617 –6623 (2010). 0003-6935 CrossRef
Anderson  R. R., Parrish  J. A., “The optics of human skin,” J. Invest. Dermatol.. 77, (1 ), 13 –19 (1981). 0022-202X CrossRef
Ishimaru  A., Wave Propagation and Scattering in Random Media. ,  Academic Press ,  New York  (1978).
Wang  L.-H., Jacques  S. L., Zheng  L.-Q., “MCML-Monte Carlo modeling of photon transport in multi-layered tissues,” Comput. Methods Programs Biomed.. 47, (2 ), 131 –146 (1995). 0169-2607 CrossRef
Prahl  S. A., van Gemert  M. J. C., Welch  A. J., “Determining the optical properties of turbid media by using the adding-doubling method,” Appl. Opt.. 32, (4 ), 559 –568 (1993). 0003-6935 CrossRef
Patterson  M. S., Chance  B., Wilson  B. C., “Time resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties,” Appl. Opt.. 28, (12 ), 2331 –2336 (1989). 0003-6935 CrossRef
Fishkin  J. B. et al., “Frequency-domain photon migration measurements of normal and malignant tissue optical properties in a human subject,” Appl. Opt.. 36, (1 ), 10 –20 (1997). 0003-6935 CrossRef
Farrell  T. J., Patterson  M. S., Wilson  B., “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys.. 19, (4 ), 879 –888 (1992). 0094-2405 CrossRef
Jacques  S. L. et al., “Video reflectometry to specify optical properties of tissue in vivo,” Proc. SPIE. IS11, , 211 –226 (1993). 0277-786X CrossRef
Wang  L., Jacques  S. L., “Use of a laser beam with an oblique angle on incidence to measure the reduced scattering coefficient of a turbid medium,” Appl. Opt.. 34, (13 ), 2362 –2366 (1995). 0003-6935 CrossRef
Kienle  A. et al., “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt.. 35, (13 ), 2304 –2314 (1996). 0003-6935 CrossRef
Lin  S.-P. et al., “Measurement of tissue optical properties by the use of oblique-incidence optical fiber reflectometry,” Appl. Opt.. 36, (1 ), 136 –143 (1997). 0003-6935 CrossRef
Utzinger  U., Richards-Kortum  R. R., “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt.. 8, (1 ), 121 –147 (2003). 1083-3668 CrossRef
Bargo  P. R. et al., “In vivo determination of optical properties of normal and tumor tissue with white light reflectance and empirical light transport model during endoscopy,” J. Biomed. Opt.. 10, (3 ), 034018  (2005). 1083-3668 CrossRef
Rajaram  N., Nguyen  T. H., Tunnell  J. W., “Lookup table-based inverse model for determining optical properties of turbid media,” J. Biomed. Opt.. 13, (5 ), 050501  (2008). 1083-3668 CrossRef
Yu  B. et al., “Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo,” J. Biomed. Opt.. 13, (6 ), 060505  (2008). 1083-3668 CrossRef
Nichols  B. S., Rajaram  N., Tunnel  J. W., “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt.. 17, (5 ), 057001  (2012). 1083-3668 CrossRef
Hennessy  R. et al., “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt.. 18, (3 ), 037003  (2013). 1083-3668 CrossRef
Jones  P. B. et al., “Simultaneous multispectral reflectance imaging and laser speckle flowmetry of cerebral blood flow and oxygen metabolism in focal cerebral ischemia,” J. Biomed. Opt.. 13, (4 ), 044007  (2008). 1083-3668 CrossRef
Zhou  C. et al., “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express. 14, (3 ), 1125 –1144 (2006). 1094-4087 CrossRef
Bouchard  M. B. et al., “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express. 17, (18 ), 15670 –15678 (2009). 1094-4087 CrossRef
Kawauchi  S. et al., “Diffuse light reflectance signals as potential indicators of loss of viability in brain tissue due to hypoxia: charge-coupled-device-based imaging and fiber-based measurement,” J. Biomed. Opt.. 18, (1 ), 015003  (2013). 1083-3668 CrossRef
Yin  C. et al., “Simultaneous detection of hemodynamics, mitochondrial metabolism and light scattering changes during cortical spreading depression in rats based on multi-spectral optical imaging,” NeuroImage. 76, , 70 –80 (2013). 1053-8119 CrossRef
Arnold  T., De Biasio  M., Leitner  R., “Hyper-spectral video endoscope for intra-surgery tissue classification using auto-fluorescence and reflectance spectroscopy,” Proc. SPIE. , 8087, , 808711  (2011). 0277-786X CrossRef
Basiri  A. et al., “Use of a multi-spectral camera in the characterization of skin wounds,” Opt. Express. 18, (4 ), 3244 –3257 (2010). 1094-4087 CrossRef
Hardeberg  J. Y., “Acquisition and Reproduction of Color Images: Colorimetric and Multispectral Approaches,” PhD Thesis, Ecole Nationale Superieure des Telecommunications, Paris, France (1999).
Hardeberg  J. Y., Schmitt  F., Brettel  H., “Multispectral color image capture using liquid crystal tunable filter,” Opt. Eng.. 41, (10 ), 2532 –2548 (2002). 0091-3286 CrossRef
Cheung  V. et al., “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am.. A22, (7 ), 1231 –1240 (2005). 0030-3941 CrossRef
Shen  H. L., Xin  J. H., “Spectral characterization of a color scanner based on optimized adaptive estimation,” J. Opt. Soc. Am. A. 23, (7 ), 1566 –1569 (2006). 0740-3232 CrossRef
Shen  H. L., Xin  J. H., Shao  S. J., “Improved reflectance reconstruction for multispectral imaging by combining different techniques,” Opt. Express. 15, (9 ), 5531 –5536 (2007). 1094-4087 CrossRef
König  F., “Reconstruction of natural spectra from color sensor using nonlinear estimation methods,” in  IS&T Annual Conference; The Society for Imaging Science and Technology , pp. 454 –457,  Cambridge, USA  (1997).
Stigell  P., Miyata  K., Kansari  M. H., “Wiener estimation method in estimating of spectral reflectance from RGB image,” Pattern Recogn. Image Anal.. 17, (2 ), 233 –242 (2007). 1054-6618 CrossRef
Murakami  Y. et al., “Color reproduction from low-SNR multispectral images using spatio-spectral Wiener estimation,” Opt. Express. 16, (6 ), 4106 –4120 (2008). 1094-4087 CrossRef
Chen  S., Liu  Q., “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new color for tissue measurements,” J. Biomed. Opt.. 17, (3 ), 030501  (2012). 1083-3668 CrossRef
Hernández-Andrés  J., Romero  J., Lee  R. L.  Jr., “Colorimetric and spectroradiometric characteristics of narrow-field-of-view clear skylight in Granada, Spain,” J. Opt. Soc. Am. A. 18, (2 ), 412 –420 (2001). 0740-3232 CrossRef
Mourant  J. R. et al., “Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics,” Appl. Opt.. 37, (16 ), 3586 –3593 (1998). 0003-6935 CrossRef
Abookasis  D. et al., “Imaging cortical absorption, scattering, and hemodynamic response during ischemic stroke using spatially modulated near-infrared illumination,” J. Biomed. Opt.. 14, (2 ), 024033  (2009). 1083-3668 CrossRef
Prahl  S. A., Tabulated Molar Extinction Coefficient for Hemoglobin in Water. , http://omlc.ogi.edu/spectra/hemoglobin/summary.html (18  November 2014).
Tuchin  V., Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. , 2nd ed.,  SPIE Press ,  Bellingham, WA  (2007).
Nishidate  I., Aizu  Y., Mishina  H., “Estimation of melanin and hemoglobin in skin tissue using multiple regression analysis aided by Monte Carlo simulation,” J. Biomed. Opt.. 9, (4 ), 700 –710 (2004). 1083-3668 CrossRef
Sato  S. et al., “Real-time optical diagnosis of the rat brain exposed to a laser-induced shock wave: observation of spreading depolarization, vasoconstriction and hypoxemia-oligemia,” PLoS One. 9, (1 ), e82891  (2014). 1932-6203 CrossRef
Lipton  P., “Ischemic cell death in brain neuron,” Physiol. Rev.. 79, (4 ), 1432 –1568 (1999). 0031-9333 
Jarvis  C. R., Anderson  T. R., Andrew  R. D., “Anoxic depolarization mediates acute damage independent of glutamate in neocortical brain slices,” Cereb. Cortex. 11, (3 ), 249 –259 (2001). 1047-3211 CrossRef
Joshi  I., Andrew  R. D., “Imaging anoxic depolarization during ischemia-like conditions in the mouse hemi-brain slice,” J. Neurophysiol.. 85, (1 ), 414 –424 (2001). 0022-3077 
Polischuk  T. M., Jarvis  C. R., Andrew  R. D., “Intrinsic optical signaling denoting neuronal damage in response to acute excitotoxic insult in the hippocampal slice,” Neurobiol. Dis.. 4, (6 ), 423 –437 (1998). 0969-9961 CrossRef
Jarvis  C. R. et al., “Interpretation of intrinsic optical signals and calcein fluorescence during acute excitotoxic insult in the hippocampal slice,” NeuroImage. 10, (4 ), 357 –372 (1999). 1053-8119 CrossRef
Obeidat  A., Jarvis  C. R., Andrew  R. D., “Glutamate does not mediate acute neuronal damage following spreading depression induced by O2/glucose deprivation in the hippocampal slice,” J. Cereb. Blood Flow Metab.. 20, (2 ), 412 –422 (2000). 0271-678X CrossRef
Friebel  M. et al., “Influence of oxygen saturation on the optical scattering properties of human red blood cells in the spectral range 250 to 2000 nm,” J. Biomed. Opt.. 14, (3 ), 034001  (2009). 1083-3668 CrossRef

Keiichiro Yoshida is a PhD student in the Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Japan, where he received his MS degree in engineering in 2012.

Izumi Nishidate is an associate professor in the Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology. He received his MS and PhD degrees from Muroran Institute of Technology, Japan. His research interests include diffuse reflectance spectroscopy, light transport in biological tissues, multispectral imaging, and functional imaging of skin and brain tissues.

Tomohiro Ishizuka is a MS student in the Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Japan.

Satoko Kawauchi is an assistant professor in the Division of Biomedical Information Sciences, National Defense Medical College Research Institute, Japan. She completed her MS degree at Keio University and received a PhD degree in medicine from Kyorin University, Japan. Her research interests encompass functional imaging of brain, photodynamic therapy, and diagnosing/imaging tissue viability using light scattering.

Shunichi Sato is an associate professor in the Division of Biomedical Information Sciences, National Defense Medical College Research Institute, Japan. He received his MS and PhD degrees from Keio University, Japan. His research interests include photomechanical waves and their application to gene/drag delivery systems, photoacoustic imaging, photodynamic therapy, as well as diagnosing/imaging brain tissue viability.

Manabu Sato is a professor in the Graduate School of Science and Engineering, Yamagata University, Japan. He received his MS and PhD degrees from Tohoku University, Japan. His research focus is on optical coherence tomography, spectral imaging, and their applications to imaging of intrinsic optical signals in brain, as well as development of instruments with optical fiber for biological tissues.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation

Keiichiro Yoshida ; Izumi Nishidate ; Tomohiro Ishizuka ; Satoko Kawauchi ; Shunichi Sato, et al.
"Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera", J. Biomed. Opt. 20(5), 051026 (Jan 23, 2015). ; http://dx.doi.org/10.1117/1.JBO.20.5.051026


Figures

Graphic Jump LocationF3 :

(a) Schematic diagram of the experimental system and (b) representation of the rat skull with an RGB image of the exposed rat brain.

Graphic Jump LocationF2 :

Regression coefficients versus concentrations of oxygenated hemoglobin CHbO and deoxygenated hemoglobin CHbO, coefficient a, and exponent b, obtained from the Monte Carlo simulations. (a) Regression coefficient αHbO versus concentration CHbO for various values of a. (b) Regression coefficient αHbR versus concentration CHbR for various values of a. (c) Regression coefficient αHbR versus concentration CHbO for various values of b. (d) Regression coefficient αHbR versus concentration CHbR for various values of b. (e) Regression coefficient α0 versus coefficient a for various values of CHbO and CHbR. (f) Regression coefficient α0 versus exponent b for various values of CHbO and CHbR.

Graphic Jump LocationF11 :

Comparison between the measured reflectance spectrum and the predicted reflectance spectrum for the μa and μs, as shown in Fig. 10.

Graphic Jump LocationF13 :

Time courses of ΔCHbO, ΔCHbR, ΔCHbT, and ΔStO2 averaged over the ROI in the parenchyma region shown in Fig. 9 while varying FiO2.

Graphic Jump LocationF14 :

Time averages over the period of normoxia, hyperoxia, anoxia 1 (before RA), and anoxia 2 (after RA) after respiratory arrest for (a) ΔCHbO, (b) ΔCHbR, (c) ΔCHbT, and (d) ΔStO2 averaged over the ROIs for all five samples. The error bars show the standard deviations (n=5). *P<0.05.

Graphic Jump LocationF1 :

Flow diagram of the process for estimating the concentration of oxygenated hemoglobin CHbO, the concentration of deoxygenated hemoglobin CHbR, coefficient a, exponent b, absorption coefficient μa(λ), and reduced scattering coefficient μs(λ). (a) Preparation work for determining the regression equations and (b) main process for deriving μa(λ) and μs(λ) from the reflectance spectrum r(λ) estimated by the Wiener estimation method (WEM).

Graphic Jump LocationF4 :

Comparisons between the estimated and given values for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b, obtained from the phantom experiments.

Graphic Jump LocationF5 :

Comparisons between the estimated and given values for (a) absorption coefficient μa and (b) reduced scattering coefficient μs, obtained from the phantom experiments.

Graphic Jump LocationF6 :

Typical estimated multispectral images r(λ) of the exposed rat brain under normoxia.

Graphic Jump LocationF7 :

Comparison of the estimated reflectance spectra averaged over the ROIs in Fig. 4 (white squares) and the reference spectra measured by spectrometer for (a) normoxia, (b) hyperoxia, and (c) anoxia.

Graphic Jump LocationF8 :

Estimated images of exposed rat brain obtained using the proposed method under normoxia for (a) oxygenated hemoglobin CHbO, (b) deoxygenated hemoglobin CHbR, (c) total hemoglobin CHbT, (d) tissue oxygen saturation StO2, (e) coefficient a, and (f) exponent b.

Graphic Jump LocationF9 :

Typical estimated images of exposed rat brain obtained using the proposed method under normoxia for (a) absorption coefficient μa(λ) and (b) reduced scattering coefficient μs(λ).

Graphic Jump LocationF10 :

Estimated spectra averaged over the ROI (white square in Fig. 9) for (a) absorption coefficient μa(λ) and (b) reduced scattering coefficient μs(λ). The error bars mean the standard deviations for all pixels in the ROI.

Graphic Jump LocationF12 :

Typical images for in vivo results while varying FiO2 (from top to bottom: RGB image, CHbO, CHbR, CHbT, StO2, a, and b).

Graphic Jump LocationF15 :

Time courses of Δa, Δb, Δμs(500), and Δμs(760) averaged over the ROI in the parenchyma region shown in Fig. 9 while varying FiO2.

Graphic Jump LocationF16 :

Time averages over the period of normoxia, hyperoxia, anoxia 1 (before RA), and anoxia 2 (after RA) after respiratory arrest for (a) Δa, (b) Δb, (c) Δμs(500), and (d) Δμs(760) averaged over the ROIs for all five samples. The error bars show the standard deviations (n=5). *P<0.05.

Tables

Table Grahic Jump Location
Table 1The goodness-of-fit coefficient (GFC) obtained from the five samples.

References

Bonhoeffer  T., Grinvald  A., Brain Mapping: The Methods. ,  Academic Press ,  San Diego  (1996).
Nishidate  I., Yoshida  K., Sato  M., “Changes in optical properties of rat cerebral cortical slices during oxygen glucose deprivation,” Appl. Opt.. 49, (34 ), 6617 –6623 (2010). 0003-6935 CrossRef
Anderson  R. R., Parrish  J. A., “The optics of human skin,” J. Invest. Dermatol.. 77, (1 ), 13 –19 (1981). 0022-202X CrossRef
Ishimaru  A., Wave Propagation and Scattering in Random Media. ,  Academic Press ,  New York  (1978).
Wang  L.-H., Jacques  S. L., Zheng  L.-Q., “MCML-Monte Carlo modeling of photon transport in multi-layered tissues,” Comput. Methods Programs Biomed.. 47, (2 ), 131 –146 (1995). 0169-2607 CrossRef
Prahl  S. A., van Gemert  M. J. C., Welch  A. J., “Determining the optical properties of turbid media by using the adding-doubling method,” Appl. Opt.. 32, (4 ), 559 –568 (1993). 0003-6935 CrossRef
Patterson  M. S., Chance  B., Wilson  B. C., “Time resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties,” Appl. Opt.. 28, (12 ), 2331 –2336 (1989). 0003-6935 CrossRef
Fishkin  J. B. et al., “Frequency-domain photon migration measurements of normal and malignant tissue optical properties in a human subject,” Appl. Opt.. 36, (1 ), 10 –20 (1997). 0003-6935 CrossRef
Farrell  T. J., Patterson  M. S., Wilson  B., “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys.. 19, (4 ), 879 –888 (1992). 0094-2405 CrossRef
Jacques  S. L. et al., “Video reflectometry to specify optical properties of tissue in vivo,” Proc. SPIE. IS11, , 211 –226 (1993). 0277-786X CrossRef
Wang  L., Jacques  S. L., “Use of a laser beam with an oblique angle on incidence to measure the reduced scattering coefficient of a turbid medium,” Appl. Opt.. 34, (13 ), 2362 –2366 (1995). 0003-6935 CrossRef
Kienle  A. et al., “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt.. 35, (13 ), 2304 –2314 (1996). 0003-6935 CrossRef
Lin  S.-P. et al., “Measurement of tissue optical properties by the use of oblique-incidence optical fiber reflectometry,” Appl. Opt.. 36, (1 ), 136 –143 (1997). 0003-6935 CrossRef
Utzinger  U., Richards-Kortum  R. R., “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt.. 8, (1 ), 121 –147 (2003). 1083-3668 CrossRef
Bargo  P. R. et al., “In vivo determination of optical properties of normal and tumor tissue with white light reflectance and empirical light transport model during endoscopy,” J. Biomed. Opt.. 10, (3 ), 034018  (2005). 1083-3668 CrossRef
Rajaram  N., Nguyen  T. H., Tunnell  J. W., “Lookup table-based inverse model for determining optical properties of turbid media,” J. Biomed. Opt.. 13, (5 ), 050501  (2008). 1083-3668 CrossRef
Yu  B. et al., “Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo,” J. Biomed. Opt.. 13, (6 ), 060505  (2008). 1083-3668 CrossRef
Nichols  B. S., Rajaram  N., Tunnel  J. W., “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt.. 17, (5 ), 057001  (2012). 1083-3668 CrossRef
Hennessy  R. et al., “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt.. 18, (3 ), 037003  (2013). 1083-3668 CrossRef
Jones  P. B. et al., “Simultaneous multispectral reflectance imaging and laser speckle flowmetry of cerebral blood flow and oxygen metabolism in focal cerebral ischemia,” J. Biomed. Opt.. 13, (4 ), 044007  (2008). 1083-3668 CrossRef
Zhou  C. et al., “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express. 14, (3 ), 1125 –1144 (2006). 1094-4087 CrossRef
Bouchard  M. B. et al., “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express. 17, (18 ), 15670 –15678 (2009). 1094-4087 CrossRef
Kawauchi  S. et al., “Diffuse light reflectance signals as potential indicators of loss of viability in brain tissue due to hypoxia: charge-coupled-device-based imaging and fiber-based measurement,” J. Biomed. Opt.. 18, (1 ), 015003  (2013). 1083-3668 CrossRef
Yin  C. et al., “Simultaneous detection of hemodynamics, mitochondrial metabolism and light scattering changes during cortical spreading depression in rats based on multi-spectral optical imaging,” NeuroImage. 76, , 70 –80 (2013). 1053-8119 CrossRef
Arnold  T., De Biasio  M., Leitner  R., “Hyper-spectral video endoscope for intra-surgery tissue classification using auto-fluorescence and reflectance spectroscopy,” Proc. SPIE. , 8087, , 808711  (2011). 0277-786X CrossRef
Basiri  A. et al., “Use of a multi-spectral camera in the characterization of skin wounds,” Opt. Express. 18, (4 ), 3244 –3257 (2010). 1094-4087 CrossRef
Hardeberg  J. Y., “Acquisition and Reproduction of Color Images: Colorimetric and Multispectral Approaches,” PhD Thesis, Ecole Nationale Superieure des Telecommunications, Paris, France (1999).
Hardeberg  J. Y., Schmitt  F., Brettel  H., “Multispectral color image capture using liquid crystal tunable filter,” Opt. Eng.. 41, (10 ), 2532 –2548 (2002). 0091-3286 CrossRef
Cheung  V. et al., “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am.. A22, (7 ), 1231 –1240 (2005). 0030-3941 CrossRef
Shen  H. L., Xin  J. H., “Spectral characterization of a color scanner based on optimized adaptive estimation,” J. Opt. Soc. Am. A. 23, (7 ), 1566 –1569 (2006). 0740-3232 CrossRef
Shen  H. L., Xin  J. H., Shao  S. J., “Improved reflectance reconstruction for multispectral imaging by combining different techniques,” Opt. Express. 15, (9 ), 5531 –5536 (2007). 1094-4087 CrossRef
König  F., “Reconstruction of natural spectra from color sensor using nonlinear estimation methods,” in  IS&T Annual Conference; The Society for Imaging Science and Technology , pp. 454 –457,  Cambridge, USA  (1997).
Stigell  P., Miyata  K., Kansari  M. H., “Wiener estimation method in estimating of spectral reflectance from RGB image,” Pattern Recogn. Image Anal.. 17, (2 ), 233 –242 (2007). 1054-6618 CrossRef
Murakami  Y. et al., “Color reproduction from low-SNR multispectral images using spatio-spectral Wiener estimation,” Opt. Express. 16, (6 ), 4106 –4120 (2008). 1094-4087 CrossRef
Chen  S., Liu  Q., “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new color for tissue measurements,” J. Biomed. Opt.. 17, (3 ), 030501  (2012). 1083-3668 CrossRef
Hernández-Andrés  J., Romero  J., Lee  R. L.  Jr., “Colorimetric and spectroradiometric characteristics of narrow-field-of-view clear skylight in Granada, Spain,” J. Opt. Soc. Am. A. 18, (2 ), 412 –420 (2001). 0740-3232 CrossRef
Mourant  J. R. et al., “Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics,” Appl. Opt.. 37, (16 ), 3586 –3593 (1998). 0003-6935 CrossRef
Abookasis  D. et al., “Imaging cortical absorption, scattering, and hemodynamic response during ischemic stroke using spatially modulated near-infrared illumination,” J. Biomed. Opt.. 14, (2 ), 024033  (2009). 1083-3668 CrossRef
Prahl  S. A., Tabulated Molar Extinction Coefficient for Hemoglobin in Water. , http://omlc.ogi.edu/spectra/hemoglobin/summary.html (18  November 2014).
Tuchin  V., Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. , 2nd ed.,  SPIE Press ,  Bellingham, WA  (2007).
Nishidate  I., Aizu  Y., Mishina  H., “Estimation of melanin and hemoglobin in skin tissue using multiple regression analysis aided by Monte Carlo simulation,” J. Biomed. Opt.. 9, (4 ), 700 –710 (2004). 1083-3668 CrossRef
Sato  S. et al., “Real-time optical diagnosis of the rat brain exposed to a laser-induced shock wave: observation of spreading depolarization, vasoconstriction and hypoxemia-oligemia,” PLoS One. 9, (1 ), e82891  (2014). 1932-6203 CrossRef
Lipton  P., “Ischemic cell death in brain neuron,” Physiol. Rev.. 79, (4 ), 1432 –1568 (1999). 0031-9333 
Jarvis  C. R., Anderson  T. R., Andrew  R. D., “Anoxic depolarization mediates acute damage independent of glutamate in neocortical brain slices,” Cereb. Cortex. 11, (3 ), 249 –259 (2001). 1047-3211 CrossRef
Joshi  I., Andrew  R. D., “Imaging anoxic depolarization during ischemia-like conditions in the mouse hemi-brain slice,” J. Neurophysiol.. 85, (1 ), 414 –424 (2001). 0022-3077 
Polischuk  T. M., Jarvis  C. R., Andrew  R. D., “Intrinsic optical signaling denoting neuronal damage in response to acute excitotoxic insult in the hippocampal slice,” Neurobiol. Dis.. 4, (6 ), 423 –437 (1998). 0969-9961 CrossRef
Jarvis  C. R. et al., “Interpretation of intrinsic optical signals and calcein fluorescence during acute excitotoxic insult in the hippocampal slice,” NeuroImage. 10, (4 ), 357 –372 (1999). 1053-8119 CrossRef
Obeidat  A., Jarvis  C. R., Andrew  R. D., “Glutamate does not mediate acute neuronal damage following spreading depression induced by O2/glucose deprivation in the hippocampal slice,” J. Cereb. Blood Flow Metab.. 20, (2 ), 412 –422 (2000). 0271-678X CrossRef
Friebel  M. et al., “Influence of oxygen saturation on the optical scattering properties of human red blood cells in the spectral range 250 to 2000 nm,” J. Biomed. Opt.. 14, (3 ), 034001  (2009). 1083-3668 CrossRef

Some tools below are only available to our subscribers or users with an online account.