Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 31, Issue 9, Pages 1809-1820Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2012.2203921
Keywords
Constrained reconstruction; dynamic imaging; low-rank matrices; partial separability modeling; real-time cardiac magnetic resonance imaging (MRI); sparsity
Categories
Funding
- National Institutes of Health [NIH-P41-EB001977, NIH-P41-EB015904, NIH-R01-EB013695, NIH-R21-EB009768]
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Partial separability (PS) and sparsity have been previously used to enable reconstruction of dynamic images from undersampled (k, t)-space data. This paper presents a new method to use PS and sparsity constraints jointly for enhanced performance in this context. The proposed method combines the complementary advantages of PS and sparsity constraints using a unified formulation, achieving significantly better reconstruction performance than using either of these constraints individually. A globally convergent computational algorithm is described to efficiently solve the underlying optimization problem. Reconstruction results from simulated and in vivo cardiac MRI data are also shown to illustrate the performance of the proposed method.
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