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Proceedings Paper

Dictionary learning for compressive parameter mapping in magnetic resonance imaging
Author(s): Benjamin Paul Berman; Mahesh Bharath Keerthivasan; Zhitao Li; Diego R. Martin; Maria I. Altbach; Ali Bilgin
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Paper Abstract

Parameter mapping is a valuable quantitative tool for soft tissue contrast. Accelerated data acquisition is critical for clinical utility, which has lead to various novel reconstruction techniques. In this work, a model-based compressed sensing method is extended to include a sparse regularization that is learned from the principal component coefficient. The principal components for a range of T2 decay curves are computed, and the coefficients of the principal components are reconstructed. These coefficient maps share coherent spatial structures, suggesting a patch{based dictionary is a well suited sparse transformation. This transformation is learned from the coefficients themselves. The proposed reconstruction is suited for non-Cartesian, multi-channel data. The dictionary constraint leads to parameter maps with less noise and less aliasing for high amounts of acceleration.

Paper Details

Date Published: 24 August 2015
PDF: 9 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 959707 (24 August 2015); doi: 10.1117/12.2187088
Show Author Affiliations
Benjamin Paul Berman, The Univ. of Arizona (United States)
Mahesh Bharath Keerthivasan, The Univ. of Arizona (United States)
Zhitao Li, The Univ. of Arizona (United States)
Diego R. Martin, The Univ. of Arizona (United States)
Maria I. Altbach, The Univ. of Arizona (United States)
Ali Bilgin, The Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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