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Journal of Healthcare Engineering
Volume 2017, Article ID 9856058, 9 pages
Research Article

Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction

1College of Physical Science and Technology, Central China Normal University, Wuhan 430079, Hubei, China
2Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, South-Central University for Nationalities, Wuhan 430074, Hubei, China
3Hubei Key Laboatory of Medical Information Analysis & Tumor Diagnosis and Treatment, Wuhan 430074, Hubei, China

Correspondence should be addressed to Shouyin Liu; nc.ude.uncc.yhp@uilys

Received 10 March 2017; Accepted 17 May 2017; Published 8 August 2017

Academic Editor: Feng-Huei Lin

Copyright © 2017 Junbo Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods.