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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Publisher | SPIE |
| Volume | 9597 |
| ISBN (Print) | 9781628417630, 9781628417630 |
| DOIs | |
| State | Published - 2015 |
| Event | Wavelets and Sparsity XVI - San Diego, United States Duration: Aug 10 2015 → Aug 12 2015 |
Other
| Other | Wavelets and Sparsity XVI |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 8/10/15 → 8/12/15 |
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Keywords
- Compressed sensing
- MRI
- Radial
- Sparsity
- T
ASJC Scopus subject areas
- Applied Mathematics
- Computer Science Applications
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
Cite this
Dictionary learning for compressive parameter mapping in magnetic resonance imaging. / Berman, Benjamin P.; Keerthivasan, Mahesh B.; Li, Zhitao; Martin, Diego R; Altbach, Maria I; Bilgin, Ali.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 9597 SPIE, 2015. 959707.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Dictionary learning for compressive parameter mapping in magnetic resonance imaging
AU - Berman, Benjamin P.
AU - Keerthivasan, Mahesh B.
AU - Li, Zhitao
AU - Martin, Diego R
AU - Altbach, Maria I
AU - Bilgin, Ali
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Compressed sensing
KW - MRI
KW - Radial
KW - Sparsity
KW - T
UR - http://www.scopus.com/inward/record.url?scp=84951310799&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951310799&partnerID=8YFLogxK
U2 - 10.1117/12.2187088
DO - 10.1117/12.2187088
M3 - Conference contribution
SN - 9781628417630
SN - 9781628417630
VL - 9597
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - SPIE
ER -