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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords