@inproceedings{47c593f410d3476aa05fafdcab8fd2c7,
title = "Dictionary learning for compressive parameter mapping in magnetic resonance imaging",
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.",
keywords = "Compressed sensing, MRI, Radial, Sparsity, T",
author = "Berman, {Benjamin P.} and Keerthivasan, {Mahesh B.} and Zhitao Li and Martin, {Diego R.} and Altbach, {Maria I.} and Ali Bilgin",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Wavelets and Sparsity XVI ; Conference date: 10-08-2015 Through 12-08-2015",
year = "2015",
doi = "10.1117/12.2187088",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Goyal, {Vivek K.} and {Van De Ville}, Dimitri and {Van De Ville}, Dimitri and Manos Papadakis and {Van De Ville}, Dimitri and Manos Papadakis and Goyal, {Vivek K.} and {Van De Ville}, Dimitri",
booktitle = "Wavelets and Sparsity XVI",
address = "United States",
}