Journal of Innovative Optical Health Sciences, 2014, 7 (3): 1450008, Published Online: Jan. 10, 2019
Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm
Fluorescence molecular tomography sparse regularization reconstruction algorithm least absolute shrinkage and selection operator
Abstract
Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels.
Jingjing Yu, Jingxing Cheng, Yuqing Hou, Xiaowei He. Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm[J]. Journal of Innovative Optical Health Sciences, 2014, 7(3): 1450008.