A Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction
For fluorescence molecular tomography (FMT), image-quality could be improved by incorporating sparsity constrain. L1 norm regularization method is proved better than L2 norm like Tikhonov regularization. However, Tikhonov method was found capable of achieving similar quality at high iteration cost by adopting zeroing strategy. By studying the reason, a Tikhonov-regularization-based projecting sparsity pursuit method was proposed which reduces the iterations significantly and achieves good image-quality. It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less over-sparsity and is more applicable for heterogeneous-target reconstruction, compared with several regularization methods implemented in this paper.
Cheng Jiaju,Luo Jianwen. A Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction[J].Chinese Optics Letters,2020,18(1):01.