Proposal of Compressed Sensing Using Nonlinear Sparsifying Transform for CT Image Reconstruction

摘要

Compressed sensing (CS) is attracting growing concerns in sparse-view CT image reconstruction. The most explored case of CS is total variation (TV) minimization. However, images reconstructed by TV usually suffer from some distortion, such as patchy artifacts, improper serrate edges and loss of image textures, especially in practical CT images. Most existing CS approaches including TV achieve image quality improvement by linear transform to object image. Considering the success of nonlinear filters in image processing such as denoising, we propose to replace linear transform with nonlinear ones in CS on sparse-view reconstructions as to obtain fiirther promotion. Median filter, bilateral filter and nonlocal means filter were respectively explored and combined in CS framework. As the iterative method, majorization-minimization (MM) based iterative-thresholding (IT) method was utilized. Experimental results with both digital and clinical images consistently demonstrated that nonlinear filter based CS has potentials in achieving fiirther image quality improvements compared with typical TV minimization.