基于体元的扩散光学层析成像(DOT)重建算法和组织体光学特性分区均匀性自然假设，发展了稳态测量模式下基于区域标识的“粗粒度”扩散光学层析成像重建算法。在60 dB、40 dB和20 dB噪声水平下利用含有区域标记的光学数字鼠模型对该算法进行了模拟验证，实现了对数字鼠各组织区域吸收系数和约化散射系数的重建。利用简化的仿体模型进行实验验证，基本实现对仿体多个区域的光学参数重建。结果表明，提出的重建算法能够大幅度改善基于体元的扩散光学层析成像重建算法反演问题的不适定性，大幅度提高其成像分辨率和量化精度，并基本能从噪声数据中恢复相应区域的光学参数。

A region-labeling approach of image reconstruction under continuous-wave mode is developed for “coarse-grain” diffuse optical tomography (DOT). The method is based on the framework of the pixel-based DOT methodology and on an assumption that different anatomical regions have their respective sets of homogeneous optical parameter distributions. The proposed scheme is validated using a three-dimensional (3D) digital mouse atlas, the optical parameters of the organs could be simultaneously reconstructed at 60 dB, 40 dB and 20 dB noise levels. Physical experiments on a phantom are also conducted to evaluate the performance of the method. The reconstructed results show that the region-labeling DOT solution greatly improves the ill-posedness of the inverse problem as well as the imaging resolution and quantitative accuracy by reducing the number of unknowns to be reconstructed. The homogeneous optical parameters of each region can be reconstructed from noisy data.