Journal Publications
| [145082] |
| Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
| Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
| Volume: <strong>6</strong>. Number: (2), |
| on pages: 1-3 |
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| DOI: 10.18416/IJMPI.2020.2009035 |
| URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
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Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
Conference Proceedings
| [145082] |
| Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
| Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
| Volume: <strong>6</strong>. Number: (2), |
| on pages: 1-3 |
| Chapter: |
| Editor: |
| Publisher: |
| Series: |
| Address: |
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| how published: |
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| DOI: 10.18416/IJMPI.2020.2009035 |
| URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
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| PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
