We submitted a paper to the ISMRM conference

Our team submitted the short manuscript “Prediction of EM Field Distribution in a Simple Homogenous Phantom at UHF MRI Using Physics-Informed Neural Networks (PINNs): Methodology in Data Generation” at the annual ISMRM Conference. Keep reading to learn more about our work.

Abstract

Specific Absorption Rate (SAR) calculation is the most crucial analysis at ultra-high-field (UHF) 7T MRI, as it is related to patient safety. Current SAR computation methods rely on computationally intensive simulations, which are often impractically long for real-time clinical use.

Goal: This study aims to develop a physics-informed neural network (PINN) capable of predicting electromagnetic (EM) field distribution at 7T MRI.

Approach: A neural network is trained using data generated from EM simulations. One of Maxwell’s equations is implemented as a physical constraint within the neural network to improve the accuracy of the field prediction.

Results: Introducing physics into neural networks enhances EM field prediction accuracy across the entire simulation domain.

Impact: This study proposes a deep learning-based method for EM field prediction, which, by significantly reducing the computational time, can enable safer and more accessible 7T MRI.