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.
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