Journal Publications
| [191970] |
| Title: Multi-Contrast MPI Matrix Compression. |
| Written by: L. Nawwas, M. Möddel, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025). |
| Volume: <strong>11</strong>. Number: (1 Suppl 1), |
| on pages: 1-2 |
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| DOI: https://doi.org/10.18416/IJMPI.2025.2503062 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/888 |
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Note: inproceedings, artifact
Abstract: Multi-contrast magnetic particle imaging (MPI) reconstructs the signal from different tracer materials or environments, resulting in multi-channel images that enable temperature or viscosity quantification. Since the multi-contrast problem is ill-posed, it is addressed by regularization methods that are commonly solved using the Kaczmarz algorithm. Unlike the single-contrast MPI problem, the multi-contrast one requires a high number of iterations to converge. Matrix compression techniques were already successfully used in single-contrast reconstruction and matrix recovery applications as in compressed sensing. Our work proposes to use matrix compression to reduce the reconstruction time needed to achieve good reconstruction quality in multi-contrast MPI.
Conference Proceedings
| [191970] |
| Title: Multi-Contrast MPI Matrix Compression. |
| Written by: L. Nawwas, M. Möddel, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025). |
| Volume: <strong>11</strong>. Number: (1 Suppl 1), |
| on pages: 1-2 |
| Chapter: |
| Editor: |
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| DOI: https://doi.org/10.18416/IJMPI.2025.2503062 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/888 |
| ARXIVID: |
| PMID: |
Note: inproceedings, artifact
Abstract: Multi-contrast magnetic particle imaging (MPI) reconstructs the signal from different tracer materials or environments, resulting in multi-channel images that enable temperature or viscosity quantification. Since the multi-contrast problem is ill-posed, it is addressed by regularization methods that are commonly solved using the Kaczmarz algorithm. Unlike the single-contrast MPI problem, the multi-contrast one requires a high number of iterations to converge. Matrix compression techniques were already successfully used in single-contrast reconstruction and matrix recovery applications as in compressed sensing. Our work proposes to use matrix compression to reduce the reconstruction time needed to achieve good reconstruction quality in multi-contrast MPI.
