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Proceedings Paper

Exploiting local low-rank structure in higher-dimensional MRI applications
Author(s): Joshua D. Trzasko
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Paper Abstract

In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra- and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

Paper Details

Date Published: 26 September 2013
PDF: 8 pages
Proc. SPIE 8858, Wavelets and Sparsity XV, 885821 (26 September 2013); doi: 10.1117/12.2027059
Show Author Affiliations
Joshua D. Trzasko, Mayo Clinic College of Medicine (United States)

Published in SPIE Proceedings Vol. 8858:
Wavelets and Sparsity XV
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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