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Blind linear models for the recovery of dynamic MRI dataFormat | Member Price | Non-Member Price |
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Paper Abstract
Classical accelerated dynamic MRI schemes rely on the sparsity or banded structure of the data in specied
transform domains (eg. Fourier space). Clearly, the utility of these schemes depend on the specic data and the
transform. For example, these methods only provide modest accelerations in free-breathing myocardial perfusion
MRI. In this paper, we discuss a novel blind linear model to recover the data when the optimal transform is
not known a-priori. Specically, we pose the simultaneous recovery of the optimal linear model/transform and
its coecients from the measurements as a non-convex optimization problem. We also introduce an ecient
majroize-minimize algorithm to minimize the cost function. We demonstrate the utility of the algorithm in
considerably accelerating free breathing myocardial perfusion MRI data.
Paper Details
Date Published: 27 September 2011
PDF: 8 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381V (27 September 2011); doi: 10.1117/12.893060
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
PDF: 8 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381V (27 September 2011); doi: 10.1117/12.893060
Show Author Affiliations
Sajan Goud Lingala, The Univ. of Iowa (United States)
Yue Hu, Univ. of Rochester (United States)
Yue Hu, Univ. of Rochester (United States)
Mathews Jacob, The Univ. of Iowa (United States)
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
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