Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning B 1 + estimation.

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Abstract

The purpose of this study is to demonstrate a method for specific absorption rate (SAR) reduction for 2D T2 -FLAIR MRI sequences at 7 T by predicting the required adiabatic radiofrequency (RF) pulse power and scaling the RF amplitude in a slice-wise fashion.

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Journal

Journal ID (iso-abbrev): Magn Reson Med

Title: Magnetic resonance in medicine

Publisher: Wiley

ISSN (Electronic): 1522-2594

ISSN (Print): 0740-3194

Publication date (Electronic): May 2021

Volume: 85

Issue: 5

Affiliations

[1 ] Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia.

[2 ] Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia.

[3 ] ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.

[4 ] School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Article

DOI: 10.1002/mrm.28590

PubMed ID: 33226685

SO-VID: e38b9be3-768f-4416-90ea-8cfb94d32fd0

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Keywords: SA2RAGE,specific absorption rate (SAR),convolutional neural network (CNN),TR-FOCI,FLAIR, B 1 + profile

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Keywords: SA2RAGE, specific absorption rate (SAR), convolutional neural network (CNN), TR-FOCI, FLAIR, B 1 + profile

Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning B 1 + estimation.

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