Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction

For fluorescence molecular tomography(FMT),image quality could be improved by incorporating a sparsity constraint.The L1 norm regularization method has been proven better than the L2 norm,like Tikhonov regularization.However,the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy.By studying the reason,a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that 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 oversparsity and is more applicable for heterogeneous-target reconstruction,compared with several regularization methods implemented in this Letter.