Recognize potential hazardous situations far from the modeled MR examination scenarios is crucial for RF safety applications and especially for deep-learning-based approaches. Last year we presented a Bayesian deep-learning approach for local SAR assessment. This approach allowed accurate local SAR estimations and returned reliable feedbacks on the error/uncertainty of the estimates for the MR examination scenario observed during training. However, it also showed the dangers of using the predicted uncertainty to identify out-of-training MR examination scenarios. In this study, we propose a simple approach to detect/reject potential erroneous local SAR predictions due to out-of-training transmit array and/or anatomical variations.
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