Exploiting local low-rank structure in higher-dimensional MRI applications

Joshua D. Trzasko

doi:10.1117/12.2027059

26 September 2013 Exploiting local low-rank structure in higher-dimensional MRI applications

Joshua D. Trzasko

Proceedings Volume 8858, Wavelets and Sparsity XV; 885821 (2013) https://doi.org/10.1117/12.2027059
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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.

Citation Download Citation

Joshua D. Trzasko "Exploiting local low-rank structure in higher-dimensional MRI applications", Proc. SPIE 8858, Wavelets and Sparsity XV, 885821 (26 September 2013); https://doi.org/10.1117/12.2027059

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