Simulation of Reflectance in Oral Tissue Using MCMatlab

Background

Optical Tissue Properties in the Literature

Tissue Layer	μa [cm⁻¹] 420 nm	μs [cm⁻¹] 420 nm	μa [cm⁻¹] 450 nm	μs [cm⁻¹] 450 nm
Superficial epithelium	3.0	170 (keratinized) / 55 (non keratinized)	2.1	140 (keratinized) / 40 (non keratinized)
Intermediate epithelium	3.0	55	2.1	40
Basal epithelium	3.0	55	2.1	40
Superficial stroma	6.22	267	3.11	248.5
Deep stroma	6.22	267	3.11	248.5
Blood	262	37.8	30	31.1

Methods

Monte Carlo Simulation Principle

A simple Monte Carlo simulation can be used to estimate the value of π by comparing areas of a circle and a square. First, the geometric domain is defined, consisting of a square whose side is equal to twice the radius $R$ of the circle, so that the circle is fully inscribed within the square (see Figure 1a). A large number of points are then generated randomly and uniformly within the square. Each point is classified according to whether it lies inside the circle or outside, and the number of points falling within the circle is counted (see Figure 1b). The ratio of the number of points inside the circle $N_{\text{circle}}$ to the total number of points in the square $N_{\text{total}}$ approaches the ratio of the areas of the circle and the square: $\frac{N_{\text{circle}}}{N_{\text{total}}}\approx \frac{\text{Area of circle}}{\text{Area of square}}=\frac{\pi R^2}{(2R{)}^2}=\frac{\pi }{4}$. Multiplying this ratio by four therefore provides an estimate of π. This example illustrates how probabilistic sampling can be used to approximate geometric quantities.

(a) Definition of the geometric domain	(b) Random point generation, classification, and counting
Figure 1: Basic Monte Carlo simulation to compute π

Monte Carlo Simulation for Light Propagation

(a) Launching a photon packet	(b) Straight-line propagation over a free path	(c) Interaction event	(d) Updating the Photon Weight
Figure 2: Monte Carlo simulation for light

Results

Influence of Keratin on the Optical Response

Fluence Results

Fluence Distribution

Figure 3: Fluence comparison between keratinized and non-keratinized tissue

Absorbance Results

Absorbance Distribution

Figure 4: Absorbance comparison between keratinized and non-keratinized tissue

Reflectance Results

Reflectance Distribution

Figure 5: Reflectance comparison between keratinized and non-keratinized tissue

Influence of Blood Depth on the Optical Response

Fluence Results

(a) Fluence at 415 nm Non-keratinized tissue	(b) Fluence at 415 nm Keratinized tissue	(c) Fluence at 450 nm Non-keratinized tissue	(d) Fluence at 450 nm Keratinized tissue
Figure 6: Fluence distribution as a function of depth for different wavelengths and tissue types

Absorbance Results

Figure 8: Absorbance as a function of blood depth for different wavelengths and tissue types

Reflectance Results

Figure 7: Reflectance as a function of blood depth for different wavelengths and tissue types

Appendix

Our code can be found at (Github link).