Blind linear models for the recovery of dynamic MRI data

Sajan Goud Lingala; Yue Hu; Mathews Jacob

doi:10.1117/12.893060

27 September 2011 Blind linear models for the recovery of dynamic MRI data

Sajan Goud Lingala, Yue Hu, Mathews Jacob

Proceedings Volume 8138, Wavelets and Sparsity XIV; 81381V (2011) https://doi.org/10.1117/12.893060
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

Citation Download Citation

Sajan Goud Lingala, Yue Hu, and Mathews Jacob "Blind linear models for the recovery of dynamic MRI data", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381V (27 September 2011); https://doi.org/10.1117/12.893060

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