Rapid Specific Absorption Rate Estimation of High-Field MRI via 3D U-net Architectures for MRI Safety

Motivation: Advancements in MRI technology towards high fields demand rapid and accurate SAR estimation tools for enhancing MRI safety, currently hindered by the computational cost of conventional physics-based simulators. Goal(s): The goal is to develop an efficient machine learning framework capable of estimating subject-specific SAR values rapidly. Approach: The study employs 3D U-net deep learning models with their variants to achieve rapid and accurate SAR estimations. Results: The proposed neural network model provides SAR estimations within 183ms, achieving approximately 10,000x acceleration over traditional physics-based simulators, with a mean relative error of 7.6%. Impact: The near real-time accurate SAR estimation achieved by proposed machine learning framework will allow (i) checking patient-specific SAR while patient is lying in the MRI machine and (ii) performing ultra-fast optimization and uncertainty quantification studies while designing new high-field coils.