Adaptive Prior Patch Size Based Sparse-view CT Reconstruction Algorithm

Compressed sensing (CS) reconstruction methods employing sparsity regularization and prior constraints are successfully applied in sparse-view computed tomography (CT) reconstruction and yield high-quality images compared with other low-dose imaging methods. In this paper, we proposed an adaptive prior patch size (APPS) strategy in sparse-view CT reconstruction. The method adopts sparse representation (SR) using adaptive patch size instead of a constant one to synthesize prior image of higher quality, because the optimal patch size should vary from each distribution range of local feature. The simulation experiments show that the proposed strategy has the better performance than the method with fixed patch size in terms of artifact reduction and edge preservation.