Conference proceeding
Blind linear models for the recovery of dynamic MRI data
Proceedings of SPIE, Vol.8138(1), pp.81381V-81381V-8
Wavelets and Sparsity XIV
09/08/2011
DOI: 10.1117/12.893060
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.
Details
- Title: Subtitle
- Blind linear models for the recovery of dynamic MRI data
- Creators
- Sajan Goud Lingala - The Univ. of Iowa (United States)Yue Hu - Univ. of Rochester (United States)Mathews Jacob - The Univ. of Iowa (United States)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.8138(1), pp.81381V-81381V-8
- Conference
- Wavelets and Sparsity XIV
- DOI
- 10.1117/12.893060
- ISSN
- 0277-786X
- Language
- English
- Date published
- 09/08/2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Radiation Oncology; Iowa Neuroscience Institute; Electrical and Computer Engineering
- Record Identifier
- 9984070407202771
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