The regularization plays an important role in the sparse-view x-ray computer tomography (CT) reconstruction. Based on the piecewise constant assumption, total variation (TV) regularization has been widely discussed for the sparse-view CT reconstruction. However, TV minimization often leads to some loss of the image edge information during reducing the image noise and arti- facts. To overcome the drawback of TV regularization, this paper proposes to introduce a novel Mumford-Shah total variation (MSTV) regularization by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is presented for the sparse-view CT reconstruction. To evaluate the performance of our PWLS-MSTV algorithm, both qualitative and quantitative analyses are executed via phantom experiments. Experimental results show that the proposed PWLS-MSTV algorithm can attain notable gains in terms of accuracy and resolution properties over the TV regularization based algorithm.
['Bo Chen', 'Chen Zhang', 'Zhaoying Bian', 'Wen-Sheng Chen', 'Jianhua Ma', 'Qing-Hua Zou', 'Xiao-Hui Zhou']
Sparse-View X-ray Computed Tomography Reconstruction via Mumford-Shah Total Variation Regularization