One of the most critical aspects that limits the application of ultra-high field MRI is the local Specific Absorption Rate (SAR) evaluation. The key aspect is that local SAR information could only be obtained by off-line simulation using generic body models, which hardly match with the patient's body and positioning. In this work we present a first deep learning approach for local SAR assessment. Results, show that the relation between local SAR on the one hand and MR Dixon images and B1-field maps on the other hand, can be accurately and instantaneously mapped by a Convolutional Neural Network (CNN).
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