 |
      |
 |
| |
"Numerical Body Model Inference for Personalized RF Exposure Prediction in Neuroimaging at 7T"
Wyger Brink, Sahar Yousefi, Prernna Bhatnagar, Marius Staring, Rob Remis and Andrew Webb
Abstract
Compliance with RF exposure limits in ultra-high field MRI is typically based on "one-size-fits-all" safety margins to account for the intersubject variability of local SAR. In this work we have developed a semantic segmentation method based on deep learning, which is able to generate a subject-specific bodymodel for personalized RF exposure prediction at 7T.
|
| |
Download |
| PDF (3 pages, 806 kB) |
|
|
Copyright © 2021 by the authors.
Published version © 2021 by .
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.
|
|
| |
BibTeX entry
| @inproceedings{Brink:2021, |
| author |
= {Brink, Wyger and Yousefi, Sahar and Bhatnagar, Prernna and Staring, Marius and Remis, Rob and Webb, Andrew}, |
| title |
= {Numerical Body Model Inference for Personalized RF Exposure Prediction in Neuroimaging at 7T}, |
| booktitle |
= {ISMRM}, |
| address |
= {Vancouver, BC, Canada}, |
| month |
= {May}, |
| year |
= {2021}, |
| } |
|
|
|
|
| last modified: 21-09-2021 |
| | webmaster |
| | Copyright 2004-2024 © by Marius Staring |
|