2.1.

OTC System

Figure 1 shows a schema of the OCT system used in this study.²⁶ Briefly, a broadband light source is a superluminescent diode with a central wavelength at $1310\mathrm{nm}$ and a bandwidth of $50\mathrm{nm}$ . The light is delivered via a single-mode fiber with a mode field diameter of $5.3\mu \mathrm{m}$ . This OCT system provides an axial resolution of $10\text{to}15\mu \mathrm{m}$ . The transverse resolution of the system is about $25\mu \mathrm{m}$ , as determined by the focal spot size produced by the probe beam. The signal-to-noise ratio (SNR) of this system is measured at $100\mathrm{dB}$ . A visible light source $(\lambda =645\mathrm{nm})$ was used to guide the probe beam. The OCT system operation is controlled automatically by computer. The detector current is demodulated using a lock-in amplifier and a low-pass filter in software prior to storage. Each in-depth scanning (A-scan) consists of 10,000 data points. The lateral scanning (B-scan) image is obtained by moving the mirror relative to the tissue sample, which takes about $1.0\mathrm{s}$ . The data acquisition software is written in LabVIEW 7.2-D. OCT images are obtained in each experiment and stored in the PC for further processing.

Fig. 1

Schematic of the OCT system: CL, collimating lens; FC, fiber coupler; PC, polarization controller; OL, objective lens; and D, detector.

2.2.

Animals

Twenty female Sprague-Dawley rats, from the Laboratory Animal Center, Guangzhou University of Traditional Chinese Medicine, weighing between 150 and $200\mathrm{g}$ were used in this study. The animals were maintained in a standard laboratory, where chow and tap water were available ad libitum for $7\text{days}$ , and then they were randomly divided into the model group $(n=10)$ and the normal control group $(n=10)$ .

In the model group, a standard chronic gastritis(Spleen-stomach Damp-heat syndrome in TCM) rat model was used.^{27, 28} Briefly, 10 rats of the model group were fed with a high-fat and sugar diet under a damp-heat environment and appropriate alcohol and axunge were alternatively perfused. The normal control group was perfused equivalent physiological saline every day and maintained in a standard environment. All rats were fed continuously for $3\text{weeks}$ .

2.3.

Experimental Procedure

Prior to performing the imaging, rats were anesthetized using 1% pentobarbital sodium $(40\mathrm{mg}∕\mathrm{kg})$ , and then each rat was put on an optical table with its mouth widely opened by forceps and 12 positions from regions A to H (Fig. 2 ) on the tongue surface were scanned by OCT. Each position was scanned more than twice, and all OCT images were stored in a computer.

Fig. 2

Regions of the rat’s tongue from A to H; A belongs to the tongue tip, which accounts for $1∕5$ of the tongue, B to G belong to the tongue middle, $3∕5$ ; and H belongs to the tongue root, $1∕5$ .

Histology was obtained immediately following the experiments. The rats were deeply anesthetized with pentobarbital sodium $(100\mathrm{mg}∕\mathrm{kg})$ . The rat tongues were extracted and fixed in $100\mathrm{ml}∕\mathrm{L}$ neutral formalin. The sample was dehydrated with ethanol, embedded in paraffin, sectioned and stained with hematoxylin and eosin (H & E) on a glass slide. The slide was examined under an optical microscope for the structure of the tongue.

2.4.

Measurement of the Thickness of the Tongue Coating

In an OCT measurement, the measured value of microstructure thickness is not the real thickness, it is the optical thickness $(l=nt)$ , where $n$ and $t$ are the refractive index and real thickness of the sample, respectively. The refractive index of biology tissues is changeable²⁹ between 1.35 and 1.45, so in this study, we chose a middle value of 1.40 as the refractive index of the tongue coating. The optical thickness is measured, and the real thickness of the tongue coating can be determined via

To obtain $l$ , an average of 50 to 100 adjacent A-scans were taken from a horizontal surface of each OCT B-scan. The 2-D image was averaged into a single curve to obtain the 1-D distribution of the mean signal (or mean gray level) in depth (Fig. 3 ). Based on differences in the OCT signal in depth, different layers of the tongue were clearly distinguished and matched with the histology. From the results of this study, we can discern that the first layer is called the tongue coating in TCM, and $l$ is the optical thickness of tongue coating from the depth axis.

Fig. 3

(a) Typical OCT image of the normal rat’s glossal surface structure and (b) corresponding 1-D OCT signal in depth: 1; tongue coating; 2, tongue body; and 3, the interface of tongue coating and tongue body.

2.5.

Measurement of Moisture Degree of the Tongue

OCT imaging is based on intensity differences of backscattered light. In this model, OCT is assumed to detect only light that has been scattered once, and therefore the decay of the OCT signal with depth simply follows the Lambert-Beer law. According to the Lambert-Beer law, light attenuation inside tissues is exponential. The attenuation coefficient $\left({\mu }_t\right)$ includes the scattering coefficient $\left({\mu }_s\right)$ and the absorption coefficient $\left({\mu }_a\right)$ . Because ${\mu }_s⪢{\mu }_a$ in the near-IR spectral range, ${\mu }_t$ is almost proportional³⁰ to ${\mu }_s$ . Tissue scattering properties are highly dependent on the ratio of the refractive index of scattering centers to the refractive index of interstitial fluid, so if the interstitial fluid of the tongue, i.e., moisture degree, is changeable, the scattering properties $\left({\mu }_t\right)$ should vary. In this study, the attenuation character of OCT signal within the tongue body is distinct. We used ${\mu }_t$ within the tongue body to assess the moisture degree of the tongue. From the 1-D distribution of the mean signal $\left(i\right)$ in depth $\left(d\right)$ , with a Levenberg-Marquardt curve fitting algorithm, Eq. 2 is fitted to the OCT signal within the tongue body. We present OCT measurements only of the attenuation coefficient of different moisture degree tongues in region C.

2.6.

Statistical Analysis

All values are presented as means $\pm \mathrm{SE}$ (standard error) for the number of samples animals. The data were analyzed by Student’s $t$ tests for unpaired data, $P<0.05$ was the minimum accepted level of significance for all groups.

3.1.

Comparison Between OCT Image and Histology of the Normal Superficial Tongue Structure

To indicate that an OCT image can really reflect the layers of tongue coating, the OCT image and the histology information were compared. An OCT image of the normal rat’s glossal surface structure is displayed in Fig. 3a. Several fine features of the tongue are apparent. Three separate layers are clearly visible: an upper layer is the tongue coating (TC) over the glossal surface; at bottom the layer, the tongue body (TB) is evident, which exhibited exponential attenuation characteristic of turbid media, and the interface between TC and TB is a darker thinner region under the scanning.

The corresponding histology is shown in Fig. 4 . Three layers are also visible from the tongue tissue: the mucous stratum which is equivalent to TC, the glossal epithelial tissue, and the muscular tissue. The TB is almost completely composed of glossal muscular tissue, and the interface is composed of glossal epithelial tissue. It is shown that compared with the histology image of the tongue, the OCT scan image can clearly demonstrate the microstructure of the tongue in vivo.

Fig. 4

Histology of the rat’s normal glossal surface structure: 1, mucous stratum; 2, epithelial tissue; 3, muscular tissue.

3.2.

Thickness of the Tongue Coating

The results for the thickness of tongue coating determined by OCT are presented in Table 1 . From the table, we can see that the tongue thickness of the model group is much greater than that of the normal control group $(P<0.01)$ . Figure 5 shows that the thickness from region A to region H has an increasing trend.

Fig. 5

Thickness of tongue coating from regions A to H. The thickness of the tongue coating shows an increasing trend from region A (tongue tip) to region H (tongue root).

Table 1

Comparison of the thickness of tongue coating.

3.3.

Tongue Moisture Degree

Figure 6 shows the scattering intensity of OCT signals versus the tissue depth; it is noticed that the signals decline rapidly as depth increases. Using Eq. 2 for fitting statistics, the $R^2$ values are 0.96775 and 0.96787 in the normal control and model groups, respectively, so both fit well with the original curve. As described in Table 2 , the ${\mu }_t$ value in model group is higher than that in the normal control group $(P<0.01)$ . This represents the fact that the scattering property of the tongue body in model group is enhanced, and the interstitial fluid of the tongue is reduced. Thus, the moisture degree of the tongue of rats with chronic gastritis is decreased, and the discrepancy has remarkable statistical significance $(P<0.01)$ .

Fig. 6

Attenuation of OCT signal scattering intensity with increases in depth. The curve represents the average A-scan over the region C in an OCT image of tongue tissue (normal and model).

Table 2

The curve fit relative values in region C.