Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although good results achieved by prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory in gradient L1-norm used original PICCS model, which leads to suffering step over-smoothing edge as result. To address above-mentioned problem, this paper proposes novel improved algorithm (NPICCS) SVCT reconstruction. proposed utilizes advantages PICCS, could recover more details. Moreover, introduces L0-norm regularization into framework, overcomes disadvantage conventional enhances capability retain fine detail. split Bregman method been resolve mathematical model. verify effectiveness large number experiments with different angles are conducted. Final experimental show that preservation, suppression, detail recovery.