Abstract #4195

Subject-specific local SAR assessment with corresponding estimated uncertainty based on Bayesian deep learning

E.F. Meliado^1,2,3, A. J. E. Raaijmakers^1,2,4, M. Maspero^2,5, M.H.F. Savenije^2,5, A. Sbrizzi^1,2, P.R. Luijten¹, and C.A.T. van den Berg^2,5

¹Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ³Tesla Dynamic Coils BV, Zaltbommel, Netherlands, ⁴Biomedical Image Analysis, Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ⁵Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands

For local SAR assessment, it is not possible to completely model the actual examination scenario, and residual experimental uncertainties are always present. Currently, no methods provide an estimate of the spatial distribution of confidence in the assessed local SAR. We present a Bayesian deep learning approach to map the relation between subject-specific complex B₁⁺-maps and the corresponding local SAR distribution, and to predict the spatial distribution of uncertainty at the same time. First results show the feasibility of the proposed approach providing potential for subject-specific reduction of uncertainties in peak local SAR assessment.

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