A reweighted total variation minimization method for few view CT reconstruction in the instant CT

Abstract

In recent years, total variation (TV) minimization method has been extensively studied as one famous way of compressed sensing (CS) based CT reconstruction algorithms. Its great success makes it possible to reduce the X-ray dose because it needs much less data comparing to conventional reconstruction method. In this work, a reweighted total variation (RwTV) instead of TV is adopted as a better proxy of L0 minimization regularization. To solve the RwTV minimization constrain reconstruction problem, we treat the raw data fidelity and the sparseness constraint separately in an alternating manner as it is often used in the TV-based reconstruction problems. The key of our method is the choice of the RwTV's weighting parameters which influence the balance between data fidelity and RwTV minimization during the convergence process. Moreover, the RwTV stopping criteria is introduced based on the SNR of reconstructed image to guarantee an appropriate iteration number for the RwTV minimization process. Furthermore the FISTA method is incorporated to achieve a faster convergence rate. Finally numerical experiments show the advantage in image quality of our approach compared to the TV minimization method while the projection data of only 10 views are used.