Abstract #3405

Safety-Optimized SAR Prediction for MRI Using Deep Learning Method at 5.0 T

Shahzeb Hayat^1,2, Shao Che^1,2,3, Jin Liu⁴, Zhuoxu Cui^1,2, Siyuan Ding⁴, Chengbo Wang³, Thomas Meersman⁵, Xiaoliang Zhang⁶, and Ye Li^1,2

¹Lauterbur Imaging Research Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, China, ³Magnetic Resonance Imaging Research Center, University of Nottingham, Ningbo, China, ⁴United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China, Shenzhen, China, ⁵Translational Imaging Group, University of Nottingham, Nottingham, United Kingdom, ⁶Department of Biomedical Engineering, State University of New York at Buffalo, New York, NY, United States

Synopsis

Keywords: AI/ML Software, Safety, Machine learning, MR safety, SAR, UHF

Motivation: In this study, the primary objective is to reduce the safety factor required for maintaining patient safety while improving SAR estimation accuracy.

Goal(s): A machine-learning-based model will provide precise SAR predictions for various human models and body sizes, enabling safer and more efficient MRI scanning.

Approach: A CycleGAN model was trained with simulated data generated from an 8-channel volume transmit array, including whole-body electromagnetic field data. Additionally, SAR values were analyzed statistically under 100 randomly transmitted conditions to determine an optimal safety factor by comparing true and predicted values

Results: A safety factor of 1.0666 was demonstrated with successive accuracy improvements.

Impact: This study offers more accurate SAR predictions that support safe, efficient MRI operations without excessive safety margins using machine learning methods. The model's precision increases patient safety and reduces scan times in high-field MRI procedures.

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