Low rank matrix recovery for real-time cardiac MRI

  • Authors:

  • Bo Zhao;Justin P. Haldar;Cornelius Brinegar;Zhi-Pei Liang

  • Affiliations:

  • Department of Electrical and Computer Engineering, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign;Department of Electrical and Computer Engineering, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign;Department of Electrical and Computer Engineering, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign;Department of Electrical and Computer Engineering, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

Real-time cardiac MRI is a very challenging problem because of limitations on imaging speed and resolution. To address this problem, the (k,t) - space MR signal is modeled as being partially separable along the spatial and temporal dimensions, which results in a rank-deficient data matrix. Image reconstruction is then formulated as a low-rank matrix recovery problem, which is solved using emerging low-rank matrix recovery techniques. In this paper, the PowerFactorization algorithm is applied to efficiently recover the cardiac data matrix. Promising results are presented to demonstrate the performance of this novel approach.