A Study On Basic Transduction Theory For Noninvasive Measurement Of Tissue Components Using Spectroscopy

Noninvasive measurement of tissue components using spectroscopy is of the utmostimportance in the clinic and practice. However, the study on the basic transductiontheory for this technology is in a relatively fundamental stage, many problemsremained unresolved. From the point of view of tissue optics, and employing thespatially resolved steady-state diffuse reflectance measurement technique, we deeplystudied the basic transduction theory for the noninvasive measurement of tissuecomponents using spectroscopy in this thesis.Firstly, we deeply and systematically studied the theories of light transport in thetissues —— Monte Carlo simulations (MCS) and diffusion approximation theory.By using the Monte Carlo modeling for multi-layered tissues (MCML), the influencesof different tissue optical properties on the diffuse reflectance profiles were analyzedand the mathematical descriptions of MCS statistical and temporal characteristicswere tabulated. In addition, we modified and unified the definitions for those keyparameters in the diffusion model to obtain the optimum predictive ability of diffusereflectance, and provided the quantitative indexes for the extent of the diffusionmodel.Then, we exploited principal component analysis (PCA), a data compressiontechnique based on statistics, to analyze the unique corresponding relationshipbetween the MCS simulated diffuse reflectance data and the tissue optical propertiesin detail, and effectively proved that PCA is feasible for extracting the maincharacteristics of the diffuse reflectance profile. We also referred to the noticeableproblems when PCA is applied to obtain the principal components of the diffusereflectance data. Furthermore, the possibilities of estimating the tissue opticalproperties from the diffuse reflectance profiles in the case of single-and two-layertissue models were demonstrated.Next, as for the inverse problem of the tissue optical properties in the single-layertissue model, we validated the predictive abilities and characteristics of the presentmost popular nonlinear inverse algorithms, nonlinear least-square fitting andtraditional neural network methods. The diffusion model was used in the nonlinearleast-square method and MCS in the neural network method. Based on the aboveresearches, a MCS-based PCA-NN method was proposed to retrieve the tissueabsorption and reduced scattering coefficients from the diffuse reflectance profile.The effectiveness of this method was preliminarily evaluated by experiments with theIntralipid-10% solution.Finally, the MCS-based PCA-NN method was extended to resolve the inverseproblem of the tissue optical properties in the multi-layered turbid media. Particularlyfor extracting the optical properties of the two layers in the two-layer tissue modelwith the top layer thickness known a priori, we proposed several PCA-NN and itsmodified algorithms which were established on the relationships between the diffusereflectance data and the optical properties of the top layer and/or the bottom layer. Thepossibility and essential conditions were also referred to for these algorithms toestimate the optical properties of the two layers from the diffuse reflectance profile.What's more, we analyzed the influence of the measurement uncertainty of the toplayer thickness on the predictive accuracies of the optical properties of the two layers,which will provide the theoretic basis for the noninvasive measurement of tissuecomponents using spectroscopy in practice.