Optimized model architecture and generalization for deep learning-based SAR prediction (MRSaiFE)

Predicting the SAR distribution in ultra-high field MRI is a crucial task to prevent tissue damage due to the hotspots, though it is challenging. The MRSaiFE deep learning framework predicts SAR based on anatomical images, but it does not guarantee model generalization due to data leakage in the training process. To improve the model, we extended the UNet architecture to include residual and inception modules in its encoder part. Further, we implemented customized loss functions, and evaluation metrics to improve the predictive performance. The results show that the model predicts SAR with an SSIM=86% and MSE=0.14% for unseen body models.