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Published in:
2015 | OriginalPaper | Chapter
Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization
Authors : Jiawen Yao, Zheng Xu, Xiaolei Huang, Junzhou Huang
Published in: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Publisher: Springer International Publishing
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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 temporal 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 convergence rate of the proposed method is
$\mathcal{O}(1/N)$
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.