Medical image computing and computer-assisted intervention – MICCAI 2015: 18th international conference Munich, Germany, october 5-9, 2015 proceedings, Part II

Abstract

In this paper, we propose a novel compressive sensing model for dynamic MR reconstruction. With total variation (TV) and nuclear norm (NN) regularization, our method can utilize both spatial and tem-poral redundancy in dynamic MR images. Due to the non-smoothness and non-separability of TV and NN terms, it is difficult to optimize the primal problem. To address this issue, we propose a fast algorithm by solving a primal-dual form of the original problem. The ergodic con-vergence rate of the proposed method is O(1/N) for N iterations. In comparison with six state-of-the-art methods, extensive experiments on single-coil and multi-coil dynamic MR data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.