Abstract #0177

Deep Learning-Based Local SAR Assessment for Body Imaging at 3T

Ettore Flavio Meliado^1,2,3, Mathijs W.I. Kikken^1,2,4, Koen M. Custers^1,4, Cornelis A.T. van den Berg^2,5, and Alexander J.E. Raaijmakers^1,2,4

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, University Medical Center Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Safety, Safety, specific absorption rate; local SAR; deep learning.

Motivation: Developing methods for local SAR estimation in patients undergoing clinical MRI could significantly enhance RF safety assessments.

Goal(s): To develop and evaluate the performance of a deep learning-based method for real-time local SAR assessment in clinical 3T MRI scans.

Approach: We trained a convolutional neural network on male and female trunk models, including arms, to map the relation between subject-specific complex B₁⁺-maps and the corresponding local SAR distribution at 3T.

Results: The proposed approach demonstrates the potential for reliable assessment of local SAR distribution at 3T. However, considerable underestimations of peak SAR values were observed in some cases.

Impact: Recent simulation studies suggest clinical MRI scans may cause greater local tissue heating than previously anticipated. Measurement-based methodologies for local SAR estimation in clinical MRI systems could contribute to safety assessments and may facilitate agreement between thermal simulations and measurements.

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