Current-to-field prediction for non-linear magnetic systems via neural networks

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.

DDC Class

621: Applied Physics

004: Computer Sciences

519: Applied Mathematics, Probabilities

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

862.pdf

Type

Main Article

Size

1.49 MB

Format

Adobe PDF

Options

Current-to-field prediction for non-linear magnetic systems via neural networks