1.

Introduction

An imbalance in oxygen supply and demand causes hypoxia in solid tumor tissues, and tumor cells in chronic hypoxia show resistance to radiotherapy and chemotherapy, which leads to a poor prognosis for cancer patients.^1–4 The treatment-resistant property of the tumor cells is a result of their adaptive response to the hypoxic environment by regulating oxygen-dependent gene expression. Recent studies have shown that hypoxia is also related to inflammation.^5,6 Inflamed lesions can often become severely hypoxic, and the oxygen-dependent metabolism is also involved in inflammatory processes. Thus, detecting tissue hypoxia can provide useful information for detailed differential diagnosis and selection of therapeutic strategies of cancerous and inflammatory lesions.

Since the hemoglobin oxygen saturation (${\mathrm{StO}}_2$) varies with the dissolved oxygen concentration (${\mathrm{dO}}_2$) in a tissue, and the absorption spectrum of the hemoglobin also varies with its ${\mathrm{StO}}_2$, hemoglobin has often been used for optical measurements of tissue oxygen level. The tissue oximeter T-Stat (Spectros Co., Portola Valley) is a representative tool for measuring tissue ${\mathrm{StO}}_2$.⁷ T-Stat has several types of probes and enables single-point measurement of tissue ${\mathrm{StO}}_2$ in vivo by illuminating tissue with white light and then analyzing the reflectance spectrum in the visible range. The performance of T-Stat has been validated by tissue-simulating phantom and animal experiments, and also by some human studies.^7–9 Measurement of tissue oxygenation may provide useful information for endoscopic diagnosis, and there have been some clinical studies using T-Stat where the ${\mathrm{StO}}_2$ of gastrointestinal tissues was endoscopically measured.^10,11 However, endoscopic application of spectroscopy-based oximetry has the following problems: (1) although oxygen concentration can be distributed heterogeneously in a cancerous lesion,¹² single-point measurement cannot provide the spatial information; and (2) capturing images of many wavelengths for reflectance spectrum analysis is time-consuming, and the results are often blurred because the target tissue does not remain in a state of rest under endoscopic observation.

Several studies have used the hyperspectral imaging method for in vivo optical imaging of ${\mathrm{StO}}_2$ distribution, using multiple images corresponding to different light wavelengths.^13,14 However, very few studies have reported on ${\mathrm{StO}}_2$ imaging using a small number of wavelengths. As an application for a fundus camera, Nakamura et al.¹⁵ presented an ${\mathrm{StO}}_2$ imaging technique for human retinal vessels using only two wavelengths: 545 and 560 nm. However, this method focuses on the simple structure of the retinal tissue, where thick blood vessels sparsely exist near the surface. For endoscopic application, the imaging target should be thin vessels embedded in a tissue with strong light scattering. Nadeau et al.¹⁶ exploited the technique of spatial frequency domain imaging (SFDI) using three wavelengths of 658, 730, and 850 nm to visualize ${\mathrm{StO}}_2$ distribution of porcine kidneys. However, SFDI needs spatially modulated illumination in principle, and it is impractical to apply the technique to common forward-viewing endoscopes.

We are currently developing an ${\mathrm{StO}}_2$ imaging technique for endoscopic application using a small number of wavelength ranges of light reflected from tissue, focusing on esophageal, gastric, and colorectal mucosa as the target tissues. We previously developed an ${\mathrm{StO}}_2$ imaging technique based on three wavelength ranges and applied it to a laser illumination endoscope system.¹⁷ Here, to argue for the application of ${\mathrm{StO}}_2$ imaging to clinical endoscopy, it is important to show that tissue ${\mathrm{StO}}_2$ can be obtained by a small number of wavelengths equivalently to a spectroscopy-based technique using many wavelengths. In this work, the physical principle of our method based on tissue optics is explained. We verified this method by observing tissue-simulating phantoms whose ${\mathrm{dO}}_2$ was controlled.

2.

Principle of ${\mathrm{StO}}_2$ Calculation

To derive tissue ${\mathrm{StO}}_2$ using a small number of spectral bands, selection of the appropriate wavelength and bandwidth of each band is essential. The algorithm of our technique employs the following four wavelength ranges: two blues (narrow bands around 450 and 470 nm), green (broad band at 500–560 nm), and red (band around 620 nm). Figure 1 shows a comparison of these spectral bands [Fig. 1(c)] with the hemoglobin absorption spectra [Fig. 1(a)] and typical mucosal scattering coefficients [Fig. 1(b)]. Regarding the dependence of these spectral bands on the tissue optical parameters, the following properties are indicated: (1) since ${\mu }_{\mathrm{s}}^{\prime }$ increases as the wavelength decreases, the 450 nm blue band is particularly sensitive to the scattering property; (2) since the 470 nm blue band corresponds to the point where the absorption gap of oxy- and deoxy-hemoglobin is locally wide, the reflectance at the 470 nm blue band is sensitive to ${\mathrm{StO}}_2$; (3) since the broad green band corresponds to the absorption peak, the reflectance at the green band is effectively sensitive to blood concentration; and (4) since the absorption at the red band is extremely small, the reflectance at the red band contains very little information about the tissue blood, and hence can be used as a reference standard.

Fig. 1

(a) ${\mu }_{\mathrm{a}}(\lambda )$ of oxy- and deoxy-hemoglobins; (b) ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ of typical gastrointestinal mucosae of esophagus,¹⁸ stomach,¹⁹ and colon;²⁰ and (c) the four spectral bands used for this technique. The spectral bands are overlaid on the graphs in (a) and (b). For the hemoglobin ${\mu }_{\mathrm{a}}(\lambda )$ values, we combined three literature values from Refs. 2122.–23 to fully match them with actual values.²⁴

The algorithm for the ${\mathrm{StO}}_2$ calculation comprises the following steps: (1) obtain the diffuse reflectance of the observed tissue at the four spectral bands, (2) calculate the ratios of the reflectance at different wavelengths, and (3) transform the ratios into the tissue characteristics (${\mathrm{StO}}_2$ and blood concentration). The physical principle based on a simple model of tissue optics is described below.

Optical properties of biological tissue are generally characterized by three quantities: the absorption coefficient ${\mu }_{\mathrm{a}}$, the scattering coefficient ${\mu }_{\mathrm{s}}$, and the anisotropy factor $g$. Here, we consider a tissue as a homogeneous semi-infinite slab that is vertically irradiated by homogeneous illumination. Based on scale invariance arguments on this model, the total diffuse reflectance of the tissue depends on only the ratio ${\mu }_{\mathrm{a}}/{\mu }_{\mathrm{s}}^{\prime }$, that $R(\lambda )=R({\mu }_{\mathrm{a}}(\lambda )/{\mu }_{\mathrm{s}}^{\prime }(\lambda ))$, where ${\mu }_{\mathrm{s}}^{\prime }={\mu }_{\mathrm{s}}(1-g)$ is the reduced scattering coefficient.²⁵ Since the hemoglobin is the dominant absorptive component in the gastrointestinal tissue, ${\mu }_{\mathrm{a}}$ can be written as

Let $S_{\mathrm{B}450}$, $S_{\mathrm{B}470}$, $S_{\mathrm{G}}$, and $S_{\mathrm{R}}$ be the obtained image signals corresponding to the four spectral bands. Here, each signal $S_i$ ($i=\mathrm{B}450$, B470, G, and R) can be written as

To investigate the dependence of each signal $S_i$ on the three tissue parameters, we simulated the diffuse reflectance of the homogeneous tissue using the software package MCML,²⁷ at various conditions of the parameters of $C$, $\beta$, and ${\mathrm{StO}}_2$. The details of simulation conditions are described in Sec. 4.1.4. Figures 2(a)–2(c) show the simulated parameter dependence of each signal in the log scale. These results indicate the inverse dependence of $S_{\mathrm{B}450}$ and $S_{\mathrm{G}}$ on $\beta$ [Fig. 2(a)], the large differences in the degree of dependence of each signal on $c_{\mathrm{Hb}}$ (hence on $C$) [Fig. 2(b)], and the strong dependence of $S_{\mathrm{B}470}$ on ${\mathrm{StO}}_2$. Based on the results, we consider the following three signal ratios in the log scale ($L_D$ is canceled here):

Fig. 2

(a)–(c) Simulated dependences of the four signals and (d)–(f) the three signal ratios on the tissue parameters. For these simulations presented here, only one parameter was varied while the other two were fixed at the standard values, defined as $\beta =1.4$, $c_{\mathrm{Hb}}=0.012$, $\sigma =14{\mathrm{cm}}^{-1}$, and ${\mathrm{StO}}_2=50\%$.

Note that the three signal ratios have markedly different dependences on the three tissue parameters. Each signal ratio does not depend on the single tissue parameter but on the three parameters simultaneously and nonlinearly. However, if we consider the three-dimensional (3-D) space spanned by $(x,y,z)$, we can define nonlinear surfaces, each of which corresponds to the same ${\mathrm{StO}}_2$ value, and ${\mathrm{StO}}_2$ can be obtained separately from other two parameters. This is the principle of our technique. The details of the transformation method of $(x,y,z)$ into ${\mathrm{StO}}_2$ are described in Sec. 4.1.4.

3.

Development of ${\mathrm{StO}}_2$ Imaging Endoscope

Based on the principle described in Sec. 2, we developed a prototype endoscope system for gastroenterological endoscopy that allowed us to obtain ${\mathrm{StO}}_2$ distribution images of tissue. As with the other types of our prototype systems,^17,28 the light source of the system provided four bands of light: two blue narrow bands at 450 and 470 nm, a green broad band, and a red band [Fig. 1(c)]. Images were acquired with an RGB color CCD (same sensor as that mounted on EG590 endoscope, Fujifilm Co., Tokyo, Japan) mounted at the endoscope tip, and the image data were transmitted to the processor and saved on the hard disk of a computer that was connected to the processor. The illumination was switched sequentially among the four bands synchronized with the video frames at a rate of $30\text{frames}/\mathrm{s}$. Switching the illumination provided different spectroscopic images of the four colors at almost the same position on the object being observed unless the object moved quickly. The ${\mathrm{StO}}_2$ images were obtained at a rate of 7.5 frames/s in almost real time. The processing time from the data acquisition to display on the monitor was about 0.1 s.

4.

Experiment

4.1.

Materials and Methods

4.1.1.

Ethics statement

Prior to the study, the protocol of the experiment was reviewed and approved by the Fujifilm Imaging Technology Center. As for the human blood used in this experiment, it was confirmed by Kohjin Bio Co. Ltd. that the informed consent for its research usage had been obtained by the donor.

4.1.2.

Tissue-simulating phantoms

We prepared tissue-simulating phantoms according to the following procedure.⁸ Precipitated erythrocytes (0.8 ml) were drawn from preserved human blood (Kohjin Bio Co., Ltd., Sakado, Japan). Then4 ml of saline was added to the erythrocytes, and the mixture was centrifuged at $1500\times \mathrm{g}$ for 2.0 min. The supernatant was subsequently pipetted away. After the process was repeated twice, 20 ml of distilled water was added to hemolyze the erythrocytes completely over 15 min. The fluid was centrifuged again, and the fluid portion was extracted to obtain the hemoglobin solution. The concentration of this solution was measured by absorptiometry, and the solution was diluted to the target concentration. We prepared three types of phantoms, whose conditions are summarized in Table 1. We added Intralipid 10% solution as the light scatterer. A total of 125 ml of the phantom liquid was put into a 100-ml flask, and 0.6 g of sugar was added. The pH was then adjusted to 7.4 by adding NaOH. The temperature of the phantom was kept at 37°C by putting the flask into a thermostat bath.

Table 1

Conditions of tissue-simulating phantoms.

	Blood volume fraction (w/v%)	Intralipid concentration (w/v%)
Type 1	1.0	0.7
Type 2	1.0	1.4
Type 3	2.0	0.7

The following procedure was used for absorptiometric measurement of the hemoglobin concentration. Some of the hemoglobin solution was put into a glass cell, and the sample was illuminated by collimated light from a halogen lamp through a light diffuser. The spectra of the transmitted light of the sample and a blank cell were measured with a spectroradiometer SR-UL1 (Topcon Technohouse Co., Tokyo, Japan), and then the transmittance spectrum of the sample was obtained. The difference in the absorbance at wavelengths of 570 and 650 nm was compared with literature data of an oxy-hemoglobin solution of a fixed concentration, and then the concentration was derived.

4.1.3.

Data and image acquisition

Figure 3 shows the experimental setup. The flask containing the liquid tissue-simulating phantom was fixed in the thermostat bath. The mouth of the flask was closed with a rubber plug with holes for probes and other devices. A flexible Clarke-type catheter microelectrode (RF CC1.R oxygen probe, Integra LifeSciences Co., Plainsboro) was used for ${\mathrm{dO}}_2$ measurement. The oxygen electrode was calibrated by measuring the current of the auxiliary calibration standard liquid according to the usage. The oxygen electrode, the auxiliary thermometer probe, and the tip of the endoscope were inserted into the flask through the holes of the rubber plug. The electrode and thermometer probe were immersed into the phantom. The tip of the endoscope was fixed 1 cm above the liquid level. Next, 0.3 g of dry yeast suspended in a small amount of the phantom liquid was put into the flask to gradually reduce the phantom ${\mathrm{dO}}_2$. The holes of the rubber plug were sealed with oil-based nonhardenable putty to keep the phantom airproof. The endoscopic sensor probe of the T-Stat tissue oximeter was inserted into the phantom through the instrumental channel of the endoscope, and the probe tip was fixed approximately 3 cm below the liquid level. We monitored the phantom ${\mathrm{StO}}_2$ with T-Stat. The phantom was constantly stirred during the experiment except when the images were acquired with the endoscope. Since the endoscope illumination affected the ${\mathrm{StO}}_2$ measurement with T-Stat, we turned on the illumination only when we acquired images with the endoscope. The illumination of the T-Stat probe was dark enough at the phantom liquid level that it did not affect the endoscopic images.

Fig. 3

Experimental setup for the tissue-simulating phantom measurements: (a) overview of the whole system and (b) the phantom in the thermostat bath.

4.1.4.

Hemoglobin oxygen saturation

We derived the ${\mathrm{StO}}_2$ image using a set of consecutive frames of images corresponding to the four different bands of the illumination. Let $S_{\mathrm{B}450}$, $S_{\mathrm{B}470}$, $S_{\mathrm{G}}$, and $S_{\mathrm{R}}$ be the obtained image signals corresponding to the four spectral bands at the same position. First, each signal of the phantom image was normalized by that of an image of the reflectance standard (WS-1-SL, Labsphere, Inc., North Sutton) to make the signal proportional to the reflectance of the phantom at the corresponding wavelength range. Next, we made three signal ratios in logarithmic form as given in Eq. (5), and then transformed the ratios into a value of ${\mathrm{StO}}_2$ using the numerical correspondence of the ratios and ${\mathrm{StO}}_2$ in the 3-D space of $(x,y,z)$ as described in Sec. 2. Performing this process for all the pixels provided the ${\mathrm{StO}}_2$ distribution image.

The transformation of $(x,y,z)$ into ${\mathrm{StO}}_2$ was performed using a lookup table, which was based on the simulated diffuse reflectance by MCML. The conditions for the simulation are summarized in Table 2. The tissue thickness was set at 5 cm, which was thick enough to consider the tissue as semi-infinitely-deep for visible light. Using the parameters in Table 2, ${\mu }_{\mathrm{a}}$ and ${\mu }_{\mathrm{s}}^{\prime }$ were calculated by Eqs. (1) and (2), respectively. We calculated the total diffuse reflectance $R(\lambda )$ at each tissue condition in the wavelength range of 400 to 700 nm at 1 nm intervals, and then simulated the normalized image signals $S_i$ ($i=\mathrm{B}450$, B470, G, and R) by the following equation using the sensor sensitivities and illumination spectra [corresponding to Eq. (4)] as

Eq. (6)

$$S_i(C,\beta ,{\mathrm{StO}}_2)=\sum _{\lambda =400\mathrm{nm}}^{700\mathrm{nm}}{L}_i(\lambda )R(C,\beta ,{\mathrm{StO}}_2;\lambda )S_{\mathrm{CCD}}(\lambda )/\sum _{\lambda =400\mathrm{nm}}^{700\mathrm{nm}}{L}_i(\lambda )S_{\mathrm{CCD}}(\lambda ),$$

where the summations over $\lambda$ were performed at 1 nm steps. The three signal ratios $(x,y,z)$ were obtained by substituting Eq. (6) into $S_i$ in Eq. (5).

Table 2

Simulation conditions to obtain total diffuse reflectance by MCML.

Refractive index	1.4
Thickness	5 cm
Volume fraction of the blood $c_{\mathrm{Hb}}$ in Eq. (1)	13 values in the range of 0.003 to 0.192 at geometrically progressive intervals by a factor of 1.414
Oxygen saturation ${\mathrm{StO}}_2$ in Eq. (1)	0 to 100% at 10% intervals
Scattering coefficient $\sigma$ in Eq. (2)	$14{\mathrm{cm}}^{-1}$
Wavelength exponent $\beta$ in Eq. (2)	0.6 to 2.2 at 0.4 intervals
Anisotropy factor $g$	0.9

Next, we considered the distribution of points $\mathrm{P}(x,y,z)$ in the 3-D space. Here, a combination of three tissue parameters $(C,\beta ,{\mathrm{StO}}_2)$ corresponded to each $\mathrm{P}(x,y,z)$, and we found that if ${\mathrm{StO}}_2$ was fixed and the other two parameters were varied, the simulated $\mathrm{P}(x,y,z)$ distributed on a mildly curved surface. This curved surface defined the iso-${\mathrm{StO}}_2$ surface for each ${\mathrm{StO}}_2$ value as presented in Fig. 4. Exploiting this property, we created the lookup table to transform $(x,y,z)$ into ${\mathrm{StO}}_2$ as follows. We approximated each surface by a third-degree polynomial of $xz$ as

Eq. (7)

$$y({\mathrm{StO}}_2;x,z)=\sum _{m,n=0}^3{a}_{mn}({\mathrm{StO}}_2)x^m{z}^n,$$

and the determined coefficients $a_{mn}({\mathrm{StO}}_2)$ were stored in the computer’s memory. Here, suppose that the obtained signal ratios by the experiment were $(x_{\text{exp}},y_{\text{exp}},z_{\text{exp}})$. First, we calculated $y({\mathrm{StO}}_2;x_{\text{exp}},z_{\text{exp}})$ values by Eq. (7) for ${\mathrm{StO}}_2=0\%-100\%$ at 10% intervals, and searched for two consecutive $y$ values $y_1$ and $y_2$ at ${\mathrm{StO}}_2={\mathrm{StO}}_2^{(1)}$ and ${\mathrm{StO}}_2^{(2)}$, respectively, that satisfied $y_1\le y_{\text{exp}}<y_2$. Then we obtained the obtained the corresponding ${\mathrm{StO}}_2$ value by a linear interpolation as

Eq. (8)

$${\mathrm{StO}}_{2\exp }=\frac{{\mathrm{StO}}_2^{(1)}(y_2-y_{\exp })+{\mathrm{StO}}_2^{(2)}(y_{\exp }-y_1)}{y_2-y_1}.$$

Fig. 4

Iso-${\mathrm{StO}}_2$ surfaces in the three-dimensional space defined by the three signal ratios $(x,y,z)$. Three surfaces at ${\mathrm{StO}}_2=0$, 50, and 100% are presented. Each mesh point corresponds to a simulated point. The directions corresponding to the changes in $\beta$ and $c_{\mathrm{Hb}}$ are indicated on the surface of ${\mathrm{StO}}_2=100\%$.

4.1.5.

Determination of optical properties

The optical properties of the phantom liquid were determined by the integrating sphere technique, using the same setup as described in Ref. 28. The phantom liquid was put into a custom-made planar glass cell with an inner spacing of 2 mm, and the total transmittance $T_t$ and diffuse reflectance $R_d$ of the 2-mm-thick liquid were measured using an integrating sphere and a spectrometer. The reduced scattering coefficient ${\mu }_{\mathrm{s}}^{\prime }$ was determined by an inverse Monte Carlo technique.¹⁹ Here, we assumed a homogeneous, 2-mm-thick tissue model possessing the optical parameters of ${\mu }_{\mathrm{a}}$ and ${\mu }_{\mathrm{s}}^{\prime }$, and calculated its $T_t$ and $R_d$ using the software package MCML. We performed the calculation for various ${\mu }_{\mathrm{a}}$ and ${\mu }_{\mathrm{s}}^{\prime }$, and determined the optical parameters that reproduced the measured values with minimal errors.

4.2.

Results

4.2.1.

StO₂ images

Figure 5 shows endoscopic images of the phantom liquid surface obtained at different ${\mathrm{StO}}_2$ conditions. We defined a region of interest on the phantom surface area for each image to eliminate inappropriate areas, such as specular reflection spots and the T-Stat probe. We then obtained the average ${\mathrm{StO}}_2$ value in the region of interest. Figure 6 shows the results of the phantom measurements, where ${\mathrm{StO}}_2$ measured with the endoscope and T-Stat are plotted against ${\mathrm{dO}}_2$. The literature values of the hemoglobin dissociation curve are also presented. Here, the dissociation curve was created using the parameters of Ref. 22 at physiologic conditions. The endoscopic ${\mathrm{StO}}_2$ values agree with those from the literature within 10%. The raw T-Stat ${\mathrm{StO}}_2$ values disagreed in the range of ${\mathrm{dO}}_2>80\mathrm{mmHg}$ and $<10\mathrm{mmHg}$, where the literature ${\mathrm{StO}}_2$ becomes nearly 100% and 0%, respectively. The raw T-Stat ${\mathrm{StO}}_2$ tended to show constant values at these ${\mathrm{dO}}_2$ ranges. Hence, we normalized the T-Stat ${\mathrm{StO}}_2$ values so that their saturated values at high and low ${\mathrm{dO}}_2$ became 100% and 0%, respectively. The normalized values agree well with the endoscopic and literature values.

Fig. 5

Endoscopic images of the phantom liquid surface. Normal and pseudocolor ${\mathrm{StO}}_2$ images are juxtaposed for each ${\mathrm{StO}}_2$ level. The defined region of interest to calculate the average ${\mathrm{StO}}_2$ is indicated by a dashed closed curve on the ${\mathrm{StO}}_2$ image at ${\mathrm{StO}}_2=100\%$ (the region is common to all the images).

Fig. 6

Relation of ${\mathrm{StO}}_2$ (endoscope and T-Stat) with ${\mathrm{dO}}_2$ (oxygen electrode) for each type of phantom. The hemoglobin dissociation curve at physiological conditions is also plotted (solid curve). The error bars show the standard deviations of the ${\mathrm{StO}}_2$ of the endoscope in the region as presented in Fig. 4: (a) type 1 (Intralipid: 0.7%, blood: 1.0%), (b) type 2 (Intralipid: 1.4%, blood: 1.0%), and (c) type 3 (Intralipid: 0.7%, blood: 2.0%).

4.2.2.

Optical properties of the phantoms

The obtained optical properties of the three types of phantoms are presented in Fig. 7, which are consistent with the phantom conditions shown in Table 1.

Fig. 7

Obtained optical properties of the three types of phantoms: (a) reduced scattering coefficients and (b) absorption coefficients.

5.

Discussion

In this work, we measured the ${\mathrm{StO}}_2$ and ${\mathrm{dO}}_2$ of each phantom with the endoscope, T-Stat, and an oxygen electrode simultaneously. We prepared three phantoms with different hemoglobin and Intralipid concentrations (Table 1). The results showed ${\mathrm{StO}}_2$ values of both the endoscope and T-Stat plotted against ${\mathrm{dO}}_2$ obtained by the oxygen electrode reproduced the physiological hemoglobin dissociation curve, and thus indicated that our method of calculating ${\mathrm{StO}}_2$ using four spectral band signals was valid in the range of tissue optical properties measured in this experiment.

In Sec. 2, we expressed the tissue reflectance [Eq. (3)] using three parameters, ${\mathrm{StO}}_2$, $c_{\mathrm{Hb}}/\sigma$, and $\beta$, by assuming a semi-infinite homogeneous tissue model, and showed that the ${\mathrm{StO}}_2$ value can be obtained using the numerical correspondence of the ratios and ${\mathrm{StO}}_2$ in the 3-D space of the three signal ratios $(x,y,z)$ as defined in Eq. (5). Here, the parameter $\beta$ is the wavelength exponent of reduced scattering coefficient ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ [Eq. (2)], which determines the degree of decrease of ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ as the wavelength increases. According to some models based on the theory of Mie scattering, the exponent $\beta$ is closely related to the scatterer size in tissue.^29–32 The value of $\beta$ is restricted theoretically and semiempirically in the range of $0.20<\beta <4.0$. Scatterers of large sizes compared with the wavelength (e.g., cells and nuclei) give small values of $\beta$, while smaller scatterers give large $\beta$. The upper limit of $\beta =4.0$ corresponds to the case of Rayleigh scattering, where the scatter size is typically smaller than the wavelength of light by one order of magnitude (e.g., collagen and elastin fibrils). Therefore, different tissue structures or components can lead to different values of $\beta$. Presently, we are going to apply our ${\mathrm{StO}}_2$ imaging technique to endoscopy of the esophagus, stomach, and colorectum. The tissue of the stomach and colorectum is glandular epithelium, where single-layer epithelial cells align along the intricate glandular pitted structure and cover the collagen-rich lamina propria, while the esophageal tissue is stratified squamous epithelium, where a thick flat layer of stratified epithelial cells covers the lamina propria. Considering the differences in structure and components of these organs, the value of $\beta$ should be changed depending on the tissue being observed. Our method may provide an appropriate value of $\beta$ for each organ by making use of the four spectral band signals.

However, if we limit the target object to one organ and fix $\beta$ at an appropriate value corresponding to the optical properties of that organ, the ${\mathrm{StO}}_2$ can be obtained using only three spectral band signals ($S_{\mathrm{B}470}$, $S_{\mathrm{G}}$, and $S_{\mathrm{R}}$), namely the two signal ratios $y$ and $z$ in Eq. (5). In Ref. 17, we reported on a clinical study of ${\mathrm{StO}}_2$ imaging using a laser illumination endoscope system. The results showed that ${\mathrm{StO}}_2$ imaging could clearly distinguish neoplasia from non-neoplasia in the esophagus and colorectum. In that clinical study, ${\mathrm{StO}}_2$ was calculated using the three spectral bands of narrow 473 nm, broad green, and broad red, fixing a parameter corresponding to $\beta$ at an organ-dependent value according to the method explained above. In regard to this point, the endoscope system presented in this work, based on the four band signals, is more advantageous since it does not need to assume a fixed parameter $\beta$, which leads to obtaining more precise ${\mathrm{StO}}_2$ values.

Recently, several spectral endoscopic imaging techniques have been developed, such as narrow band imaging^33–35 and blue laser imaging.^36–39 Both techniques exploit narrow-band blue light to enhance the increase in abnormal microvessels in the superficial mucosa, which is one of the characteristic morphological features of cancerous lesions. The technique of ${\mathrm{StO}}_2$ imaging described in this work, however, does not focus on enhancing such morphological features of the tissue, but aims at realizing a new realm of endoscopic functional imaging through visualizing tissue oxygenation. To achieve functional imaging using spectral images in endoscopy, it is essential to acquire plural spectral band images with high-speed switching of illumination wavelengths synchronized with the video frames. For practical use, the number of wavelengths should be as few as possible. The technique of this work provides a practical system using only four spectral bands to realize ${\mathrm{StO}}_2$ imaging in endoscopy. Compared with the single-point measurement of T-Stat, our ${\mathrm{StO}}_2$ imaging endoscopy has an obvious benefit—it enables real-time observation of ${\mathrm{StO}}_2$ distribution in a broad area of tissue. It is expected that this technique will be applied clinically and contribute to more detailed differential diagnosis or defining lesion boundaries in endoscopy by exploiting the functional information of tissue oxygenation.