结合流形正则和变量分离近似稀疏重构的荧光分子断层成像

为改善荧光分子断层成像的重建结果,本文采用联合稀疏-流形正则模型进行光源重建,该联合稀疏-流形正则模型能同时利用重建光源聚集性和稀疏性的先验信息。为有效求解该联合稀疏-流形正则模型,本文通过重新推导变量分离近似稀疏重构算法对其进行求解。为加快变量分离近似稀疏重构算法求解联合稀疏-流形正则模型的速度,本文在光源重建过程中采用了热启动策略。实验结果表明,相比变量分离近似稀疏重构算法求解范数模型,变量分离近似稀疏重构算法求解联合稀疏-流形正则模型将重建结果的对比噪声比从6.45提升至9.18。另外,相比没有采用热启动策略,采用热启动策略的变量分离近似稀疏重构算法求解联合稀疏-流形正则模型的时间从101.84 s减至50.10 s。本文方法显著提高了光源目标重建的精度和速度,取得了更优的重建结果。

Light source reconstruction method in fluorescence molecular tomography based on Laplacian manifold regularization and sparse reconstruction by separable approximation

To enhance reconstruction performance in fluorescence molecular tomography, a joint-norm and a Laplacian manifold regularization model that combined both sparsity and spatial aggregation information was utilized for light source reconstruction. In this report, sparse reconstruction by separable approximation (SpaRSA) was developed to investigate the joint model (SpaRSA-resolved Laplacian manifold regularization model, SpaRSALM). To improve the convergence speed of the SpaRSALM algorithm, a warm-start strategy was applied for light source reconstruction. The experimental results show that the SpaRSALM algorithm solved the joint model problem and improved the contrast to noise ratio (CNR) from 6.45 to 9.18 compared to using the SpaRSA algorithm to solve for the -norm regularization model. In addition, the reconstruction of the SpaRSALM algorithm using the warm-start strategy (compared to without the warm-start strategy) required 50.10 s (as opposed to 101.84 s). The accuracy and speed of light source reconstruction were significantly improved, and better reconstruction results were achieved using the presented method.