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"Personalised Local SAR Prediction for Parallel Transmit Neuroimaging at 7T from a Single T1-weighted Dataset"
Wyger M. Brink, Sahar Yousefi, Prerna Bhatnagar, Rob F. Remis, Marius Staring and Andrew G. Webb
Abstract
Purpose. Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultrahigh field strengths (≥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 overall Dice coefficients of 86.7% ± 6.7% (mean ± standard deviation) 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.
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Copyright © 2022 by the authors.
Published version © 2022 by International Society for Magnetic Resonance in Medicine.
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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Source code
The source code of the methods described in this paper can be found at https://github.com/wygerbrink/PersonalizedDosimetry.
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BibTeX entry
| @article{Brink:2022, |
| author |
= {Brink, Wyger M. and Yousefi, Sahar and Bhatnagar, Prerna and Remis, Rob F. and Staring, Marius and Webb, Andrew G.}, |
| title |
= {Personalised Local SAR Prediction for Parallel Transmit Neuroimaging at 7T from a Single T1-weighted Dataset}, |
| journal |
= {Magnetic Resonance in Medicine}, |
| volume |
= {88}, |
| number |
= {1}, |
| pages |
= {464 - 475}, |
| month |
= {July}, |
| year |
= {2022}, |
| } |
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| last modified: 11-05-2022 |
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