Fluorescence Diffuse Optical Tomography Reconstruction Based on Group Sparse Regularization

A simultaneous algebraic reconstruction technique based on group sparse regularization (GSR-SART) algorithm is proposed in this study to address the problems of large positioning error of the fluorescent light source and incomplete morphological information in fluorescence diffuse optical tomography. The algorithm uses nonlocal self-similarity and intrinsic local sparsity to construct the self-adaptive similar group. Then, the similar group is considered the basic unit to learn the adaptive dictionary. Finally, the target function is solved using the iteration shrinkage threshold algorithm. The experimental results show that compared with the other advanced algorithm, the proposed algorithm yields better results in terms of peak signal-to-noise ratio and root mean square error.