Accelerated MR parameter mapping with low‐rank and sparsity constraints. Issue 2 (27th August 2014)

• Record Type: Journal Article
• Authors: Zhao, Bo; Lu, Wenmiao; Hitchens, T. Kevin; Lam, Fan; Ho, Chien; Liang, Zhi‐Pei
• Is Part Of: Magnetic resonance in medicine. Volume 74:Issue 2(2015:Aug.)
• Page Start: 489
• Page End: 498
• Publication Date: 2014-08-27
• Subjects: Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.25421
• Languages: English
• ISSNs: 0740-3194

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="mrm25421-sec-0001" sec-type="section"> <title>Purpose</title> <p>To enable accurate magnetic resonance (MR) parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling.</p> </sec> <sec id="mrm25421-sec-0002" sec-type="section"> <title>Theory and Methods</title> <p>A new constrained reconstruction method based on low‐rank and sparsity constraints is proposed to accelerate MR parameter mapping. More specifically, the proposed method simultaneously imposes low‐rank and joint sparse structures on contrast‐weighted image sequences within a unified mathematical formulation. With a pre‐estimated subspace, this formulation results in a convex optimization problem, which is solved using an efficient numerical algorithm based on the alternating direction method of multipliers.</p> </sec> <sec id="mrm25421-sec-0003" sec-type="section"> <title>Results</title> <p>To evaluate the performance of the proposed method, two application examples were considered: (i) <italic>T</italic><sub>2</sub> mapping of the human brain and (ii) <italic>T</italic><sub>1</sub> mapping of the rat brain. For each application, the proposed method was evaluated at both moderate and high acceleration levels. Additionally, the proposed method was compared with two state‐of‐the‐art methods that only use a single low‐rank or joint sparsity constraint. The results demonstrate that the proposed method can achieve accurate parameter estimation with both moderately and highly undersampled data. Although all methods performed fairly well with moderately undersampled data, the proposed method achieved much better performance (e.g., more accurate parameter values) than the other two methods with highly undersampled data.</p> </sec> <sec id="mrm25421-sec-0004" sec-type="section"> <title>Conclusions</title> <p>Simultaneously imposing low‐rank and sparsity constraints can effectively improve the accuracy of fast MR parameter mapping with sparse sampling. Magn Reson Med 74:489–498, 2015. © 2014 Wiley Periodicals, Inc.</p> </sec> </abstract>