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Abstract #0293

On-line Subject-Specific Local SAR Assessment by Deep Learning

E.F. Meliado1,2, A.J.E. Raaijmakers1,3, M.H.F. Savenije1, A. Sbrizzi1, M. Maspero1, P.R. Luijten1, and C.A.T. van den Berg1

1Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 2MR Code BV, Zaltbommel, Netherlands, 3Biomedical Image Analysis, Eindhoven University of Technology, Eindhoven, Netherlands

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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