New inverse fitting model and investigating in-vivo diffuse reflectance spectra by Monte Carlo: oral mucosa

Diffuse reflectance spectroscope is a non-invasive method to detect cancer. People use this technique to develop early cancer diagnosis. Patients can accept treatments in time and reduce risk by pre-cancer screening. When carcinoma in situ or even earlier lesions, the tissue pathological changing cannot be detected by visual examination. However, tissue changing under surface corresponds on the different optical properties. The interactions of photon in tissue can be simulated by Monte Carlo method. We can generated the different diffuse reflectance spectra by assigning optical properties. Target spectra are diffuse reflectance spectra which the optical parameters are unknown. Through fitting simulative spectra with target spectra, we can analyze the unknown of target spectra. By detecting diffuse reflectance spectra from oral cavity, we can diagnose early cancer and even quantify optical properties of mucosal tissue. In this thesis, the main concept is different range of diffuse reflectance which can provide different information of optical properties. There are two majored purposes in this thesis with the main concept: One is developing the new inverse fitting model, the other is investigating in vivo diffuse reflectance spectra. Traditional iterative curve fitting is unsteady and time-consuming by Monte Carlo method. We design a new fitting model which use sensitive features of wavelength depending and combine with looked-up table. Through new inverse fitting model, target spectrum is firstly estimated the initial parameters set, then iteratively calculated by matrix. Finally, we can effectively extract optical properties from target spectrum without as many as times of Monte Carlo method. Diffuse reflectance spectra, from human oral mucosa, are acquired by three optical fibers at different source-detector separations. We extract the optical properties from measured spectra by Monte Carlo and fitting processing. However, there are errors, 19-29%, between simulated fitting and measured target. It cannot be improved by only adjusting inverse fitting model. We are going to research forward model because the different, between simulation and reality, should come from the tissue model used in Monte Carlo calculation. In this part of research, there are discussing of spectra different, surveying tissue model, and adjusting calculated method. Try to approach the simulative model to real mucosal tissue.