本論文針對應用於乳癌診斷之擴散光學斷層掃描系統，發展組織光學係數影像重建
演算法，並藉由乳房攝影之組織結構影像訊息，提昇光學係數影像之解析度。以擴散方
程式為模型，將演算法分為前向計算與反向重建二階段。其中，於前向計算使用有限元
素法求解擴散方程式，以獲得所需位置之理論光強度；於反向重建使用空間映射正則化
反算程序，帶入校正後之實測光強度，再經疊代最小化理論與實測光強度之差異，求得
光學係數之變化量。藉由設計不同型態之腫瘤假體案例，經數值模擬與假體實驗，驗證
本研究檢知腫瘤對比度、大小及位置等特性之能力，並以均方誤差、一維剖面曲線及解
析度分析，量化評估影像品質。從研究中得知，使用Tikhonov 正則化方法且使用對比
度高於背景之初始條件，可得較佳之影像品質。為助於後續之臨床相關研究，應發展估
計整體光學特性之方法，給定合理之初始條件，伴隨標準假體之研究，以協助光學特性
估計及量測系統特性校正。

This thesis aims to develop an improved image reconstruction algorithm, a dual-modality
scheme, for diffuse optical tomography (DOT) by employing the spatial information of X-ray
mammography. The conventional image reconstruction algorithm of DOT based on the
diffusion equation involves both the forward computation and the regularization of inverse
reconstruction. The forward computation uses finite element method (FEM) for solving
diffusion equation to evaluate the distribution of transmitted light on the basis of the light
source and presumed parameters of the object. The regularization of inverse reconstruction,
here, uses the structural information obtained from x-ray mammography to reconstruct the
distribution of the optical properties of the object from measured data sets. The improved
algorithm is validated using different designated cases including various size, contrast and
location of inclusions to background. As a comparison, both simulation data and phantom
experimental data are employed to reconstruct functional optical-coefficient images. Besides,
the errors of all reconstruction images were quantitatively evaluated by using a mean square
error (MSE). Furthermore, we also use a set of measures on the quantitative evaluation for a
range of resolutions including size, contrast, and location, which provide a comparable
estimate to the visual quality. These results show that the use of Tikhonov regularization
method and a higher initial contrast can provide a better quality of image reconstruction.

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