24 August 2015 Dictionary learning for compressive parameter mapping in magnetic resonance imaging
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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.
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Benjamin Paul Berman, Benjamin Paul Berman, Mahesh Bharath Keerthivasan, Mahesh Bharath Keerthivasan, Zhitao Li, Zhitao Li, Diego R. Martin, Diego R. Martin, Maria I. Altbach, Maria I. Altbach, Ali Bilgin, Ali Bilgin, } "Dictionary learning for compressive parameter mapping in magnetic resonance imaging", Proc. SPIE 9597, Wavelets and Sparsity XVI, 959707 (24 August 2015); doi: 10.1117/12.2187088; https://doi.org/10.1117/12.2187088
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