Bibliographic Details
| Title: |
Physical knowledge improves prediction of EM Fields |
| Authors: |
Dulny, Andrzej, Jabbarigargari, Farzad, Hotho, Andreas, Schreiber, Laura Maria, Terekhov, Maxim, Krause, Anna |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning |
| More Details: |
We propose a 3D U-Net model to predict the spatial distribution of electromagnetic fields inside a radio-frequency (RF) coil with a subject present, using the phase, amplitude, and position of the coils, along with the density, permittivity, and conductivity of the surrounding medium as inputs. To improve accuracy, we introduce a physics-augmented variant, U-Net Phys, which incorporates Gauss's law of magnetism into the loss function using finite differences. We train our models on electromagnetic field simulations from CST Studio Suite for an eight-channel dipole array RF coil at 7T MRI. Experimental results show that U-Net Phys significantly outperforms the standard U-Net, particularly in predicting fields within the subject, demonstrating the advantage of integrating physical constraints into deep learning-based field prediction. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.11703 |
| Accession Number: |
edsarx.2503.11703 |
| Database: |
arXiv |
