Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1‐weighted dataset

• Published in: Magnetic resonance in medicine Vol. 88; no. 1; pp. 464 - 475
• Main Authors: Brink, Wyger M., Yousefi, Sahar, Bhatnagar, Prernna, Remis, Rob F., Staring, Marius, Webb, Andrew G.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2022; John Wiley and Sons Inc
• Subjects: Artificial neural networks; body models; Customization; Data acquisition; Datasets; Deep learning; Image segmentation; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neural Networks, Computer; Neuroimaging; Phantoms, Imaging; Predictions; PTx; Quadratures; Safety margins; SAR; subject‐specific; s—Computer Processing and Modeling; body models; subject-specific; deep learning; PTx; SAR
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.29215

Purpose Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra‐high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over‐conservative safety margins to account for intersubject variability, which negatively affect the utilization of ultra‐high field MR. In this work, we present a method to generate a subject‐specific body model from a single T1‐weighted dataset for personalized local SAR prediction in PTx neuroimaging at 7T. Methods Multi‐contrast data were acquired at 7T (N = 10) to establish ground truth segmentations in eight tissue types. A 2.5D convolutional neural network was trained using the T1‐weighted data as input in a leave‐one‐out cross‐validation study. The segmentation accuracy was evaluated through local SAR simulations in a quadrature birdcage as well as a PTx coil model. Results The network‐generated segmentations reached Dice coefficients of 86.7% ± 6.7% (mean ± SD) and showed to successfully address the severe intensity bias and contrast variations typical to 7T. Errors in peak local SAR obtained were below 3.0% in the quadrature birdcage. Results obtained in the PTx configuration indicated that a safety margin of 6.3% ensures conservative local SAR estimates in 95% of the random RF shims, compared to an average overestimation of 34% in the generic “one‐size‐fits‐all” approach. Conclusion A subject‐specific body model can be automatically generated from a single T1‐weighted dataset by means of deep learning, providing the necessary inputs for accurate and personalized local SAR predictions in PTx neuroimaging at 7T.