Journal Publications
| [191963] |
| Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks. |
| Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel |
| 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.2025.2503009 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862 |
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Note: inproceedings, magneticfield, ml
Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.
Conference Proceedings
| [191963] |
| Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks. |
| Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel |
| 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: |
| Publisher: |
| Series: |
| Address: |
| Edition: |
| ISBN: |
| how published: |
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| DOI: https://doi.org/10.18416/IJMPI.2025.2503009 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862 |
| ARXIVID: |
| PMID: |
Note: inproceedings, magneticfield, ml
Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.
