Keywords: Safety, Safety, specific absorption rate; local SAR; deep learning; Bayesian deep learning; uncertainty estimation
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 time
Results: 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.
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