Motion compensated dynamic MRI reconstruction exploiting sparsity and low rank structure

Jia, Ru; Du, Heng

doi:10.1109/icsp.2016.7877788

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“…After the theory of compressed sensing (CS) [6,7] was proposed, many scholars applied it to the dynamic MR reconstruction [5,8,9]. These methods process the original data by downsampling method instead of traditional full-sampling and reduce the computing time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low‐Rank Model

Chen

Yang

Wang

2017

Mathematical Problems in Engineering

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The quality of dynamic magnetic resonance imaging reconstruction has heavy impact on clinical diagnosis. In this paper, we propose a new reconstructive algorithm based on the + model. In the algorithm, the 1 norm is substituted by the norm to approximate the 0 norm; thus the accuracy of the solution is improved. We apply an alternate iteration method to solve the resulting problem of the proposed method. Experiments on nine data sets show that the proposed algorithm can effectively reconstruct dynamic magnetic resonance images.

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