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) |
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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 |
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Country | United States |
City | San Diego |
Period | 8/10/15 → 8/12/15 |
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