Bi Bo, Han Bo, Han Weimin, Tang Jinping, Li Li
Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang 150006, China ; School of Mathematics and Statistics, Northeast Petroleum University, Daqing, Heilongjiang 163318, China.
Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang 150006, China.
Comput Math Methods Med. 2015;2015:286161. doi: 10.1155/2015/286161. Epub 2015 Jan 14.
Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standard L₂ regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existing L₁ algorithms.
扩散光学层析成像技术是一种用于小动物研究的新型分子成像技术。尽管扩散方程（DA）在某些情况下的有效性会失效，但大多数已知的重建方法仍将其用作正向模型。在这项工作中，我们使用辐射传输方程作为正向模型，该方程能准确描述生物介质中的光传播，并研究稀疏约束在解决扩散光学层析成像逆问题中的潜力。通过边界角平均测量数据和内部角平均测量数据评估了稀疏重建方法的可行性。模拟结果表明，在大多数测试案例中，采用稀疏正则化的重建在定性和定量方面都比采用标准L₂正则化的重建更可靠。结果还表明，与其他现有的L₁算法相比，分裂Bregman算法在具有稀疏正则化的DOT图像重建中具有竞争力。
