@inproceedings{f22ece94f0864d9f967663d0501c6166,
title = "Exploiting local low-rank structure in higher-dimensional MRI applications",
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.",
keywords = "Image Reconstruction, Low-Rank, MRI, Sparsity",
author = "Trzasko, {Joshua D.}",
year = "2013",
doi = "10.1117/12.2027059",
language = "English (US)",
isbn = "9780819497086",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Wavelets and Sparsity XV",
note = "Wavelets and Sparsity XV ; Conference date: 26-08-2013 Through 29-08-2013",
}