Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 33, Issue 8, Pages 1689-1701Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2014.2321190
Keywords
Compressive sensing (CS); dynamic magnetic resonance (MR) imaging; low-rank; robust principal component analysis; sparsity
Categories
Funding
- Engineering and Physical Sciences Research Council (EPSRC) [EP/H046410/1]
- National Institute for Health Research (NIHR)
- Engineering and Physical Sciences Research Council [EP/K005278/1, EP/H046410/1] Funding Source: researchfish
- EPSRC [EP/K005278/1, EP/H046410/1] Funding Source: UKRI
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Dynamic magnetic resonance imaging (MRI) is used in multiple clinical applications, but can still benefit from higher spatial or temporal resolution. A dynamic MR image reconstruction method from partial (k, t)-space measurements is introduced that recovers and inherently separates the information in the dynamic scene. The reconstruction model is based on a low-rank plus sparse decomposition prior, which is related to robust principal component analysis. An algorithm is proposed to solve the convex optimization problem based on an alternating direction method of multipliers. The method is validated with numerical phantom simulations and cardiac MRI data against state of the art dynamic MRI reconstruction methods. Results suggest that using the proposed approach as a means of regularizing the inverse problem remains competitive with state of the art reconstruction techniques. Additionally, the decomposition induced by the reconstruction is shown to help in the context of motion estimation in dynamic contrast enhanced MRI.
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