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

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

Brink, Wyger M. (Leiden University Medical Center)
Yousefi, Sahar (Leiden University Medical Center)
Bhatnagar, Prernna (Student TU Delft; Leiden University Medical Center)
Remis, R.F. (TU Delft Signal Processing Systems)
Staring, M. (Leiden University Medical Center)
Webb, A. (Leiden University Medical Center)

2022

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.

body models
deep learning
PTx
SAR
subject-specific

http://resolver.tudelft.nl/uuid:9c72caf7-58f0-42ba-963b-dcf04eaad092

https://doi.org/10.1002/mrm.29215

0740-3194

Magnetic Resonance in Medicine, 88 (1), 464-475

Institutional Repository

journal article

© 2022 Wyger M. Brink, Sahar Yousefi, Prernna Bhatnagar, R.F. Remis, M. Staring, A. Webb