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Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization

  • Jiawen Yao
  • Zheng Xu
  • Xiaolei Huang
  • Junzhou Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

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 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.

Keywords

Compressive Sensing Nuclear Norm Coil Sensitivity Temporal Cross Section Anisotropic Total Variation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiawen Yao
    • 1
  • Zheng Xu
    • 1
  • Xiaolei Huang
    • 2
  • Junzhou Huang
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Computer Science and Engineering DepartmentLehigh UniversityBethlehemUSA

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