Rapid Generation of Subject‐Specific Human Models With Detailed Tissue Structures for Timely Individualized SAR Assessment

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ABSTRACT Purpose To enable the rapid generation of subject‐specific whole‐body anatomical models for patient‐specific prediction of torso–local specific absorption rate (SAR) in MRI. Methods A 6‐s 3D gradient‐echo MRI sequence was used to acquire data within the imaging field of view (FOV). Major tissue types were automatically segmented using a deep learning model trained via a semi‐supervised strategy combining teacher–student learning and partial‐category annotations. A full‐body geometry was reconstructed from depth data captured by a 3D camera, thereby extending the model beyond the FOV. The MRI‐derived anatomical segmentation and camera‐based external geometry were co‐registered and fused into a seamless, subject‐specific human model. Results Human models were generated in approximately 20 s per subject, including MRI acquisition and processing. Accurate tissue segmentation and robust body reconstruction were achieved. Validation on the Duke numerical phantom yielded an average peak SAR 10g error < 2%. In vivo field comparisons in 20 volunteers showed a normalized root‐mean‐square error (NRMSE) of 9.50%. The models preserved subject‐specific anatomy and were suitable for electromagnetic simulation. Conclusion A hybrid framework integrating ultrafast MRI, depth data scanning and deep learning enables rapid construction of subject‐specific human models, supporting practical, online SAR monitoring in clinical MRI.

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