Journal Publications
| [190507] |
| Title: Solving the MPI reconstruction problem with automatically tuned regularization parameters. |
| Written by: K. Scheffler, M. Boberg, and T. Knopp |
| in: <em>Physics in Medicine & Biology</em>. January (2024). |
| Volume: <strong>69</strong>. Number: (4), |
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| DOI: 10.1088/1361-6560/ad2231 |
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Note: article, openaccess
Abstract: In the field of medical imaging, Magnetic Particle Imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.
Conference Proceedings
| [190507] |
| Title: Solving the MPI reconstruction problem with automatically tuned regularization parameters. |
| Written by: K. Scheffler, M. Boberg, and T. Knopp |
| in: <em>Physics in Medicine & Biology</em>. January (2024). |
| Volume: <strong>69</strong>. Number: (4), |
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| DOI: 10.1088/1361-6560/ad2231 |
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Note: article, openaccess
Abstract: In the field of medical imaging, Magnetic Particle Imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.
