Local SAR and Uncertainty Estimation for Brain Imaging by Bayesian Deep Learning

Motivation: Local SAR cannot be measured during an MRI examination. Deep learning approaches are proving to be a solution for on-line subject-specific SAR assessment. Goal(s): The brain is the region of greatest clinical interest for ultra-high field MRI. Therefore, we apply for brain imaging a deep learning approach presented for local SAR assessment for body imaging. Approach: The Bayesian deep-learning approach maps the relation between subject-specific complex B1+-maps and the corresponding local SAR distribution, and predicts the spatial distribution of uncertainty at the same timeResults: The Bayesian deep-learning approach for local SAR assessment in brain outperforms the previous application for prostate imaging. Impact: The application of Bayesian deep-learning can allow the reduction of overly conservative RF safety constraints that limit the performance of UHF-MRI. Furthermore, the joint estimation of uncertainty can help the acceptance of such methods in clinical contexts.