Meeting Banner
Abstract #3246

Prediction of EM Field in a Simple Homogenous Phantom at UHF MRI Using Physics-Informed Neural Networks (PINNs): Methodology in Data Generation

Farzad Jabbarigargari1, Andrzej Dulny2, Maxim Terekhov1, Anna Krause2, Andreas Hotho2, and Laura Maria Schreiber1
1Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center, University Hospital Wuerzburg, Wuerzburg, Germany, 2Chair of Data Science, Center for Artificial Intelligence and Data Science (CAIDAS), University of Wuerzburg, Wuerzburg, Germany

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software

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

Goal(s): 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.

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords