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| Content Provider | Society for Industrial and Applied Mathematics (SIAM) |
|---|---|
| Author | Goyal, Vivek K. Zelinski, Adam C. Adalsteinsson, Elfar |
| Copyright Year | 2010 |
| Abstract | A problem that arises in slice-selective magnetic resonance imaging (MRI) radio-frequency (RF) excitation pulse design is abstracted as a novel linear inverse problem with a simultaneous sparsity constraint. Multiple unknown signal vectors are to be determined, where each passes through a different system matrix and the results are added to yield a single observation vector. Given the matrices and lone observation, the objective is to find a simultaneously sparse set of unknown vectors that approximately solves the system. We refer to this as the multiple-system single-output (MSSO) simultaneous sparse approximation problem. This manuscript contrasts the MSSO problem with other simultaneous sparsity problems and conducts an initial exploration of algorithms with which to solve it. Greedy algorithms and techniques based on convex relaxation are derived and compared empirically. Experiments involve sparsity pattern recovery in noiseless and noisy settings and MRI RF pulse design. |
| Starting Page | 4533 |
| Ending Page | 4579 |
| Page Count | 47 |
| File Format | |
| ISSN | 10648275 |
| DOI | 10.1137/080730822 |
| e-ISSN | 10957197 |
| Issue Number | 6 |
| Volume Number | 31 |
| Language | English |
| Publisher | Society for Industrial and Applied Mathematics |
| Publisher Date | 2010-01-20 |
| Access Restriction | Subscribed |
| Subject Keyword | second-order cone programming Inverse problems iteratively reweighted least squares Signal theory sparse approximation multiple measurement vectors Applications in optimization, convex analysis, mathematical programming, economics matching pursuit simultaneous sparse approximation magnetic resonance imaging excitation pulse design iterative shrinkage |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Computational Mathematics |
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