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arXiv · 1911.08118

Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks

Shahrokh Abbasi-Rad·Kieran O'Brien·Samuel Kelly·Viktor Vegh·Anders Rodell·Yasvir Tesiram·Jin Jin·Markus Barth·Steffen Bollmann
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Abstract

Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion. Methods: We used a TR-FOCI adiabatic pulse for spin inversion in a T2-FLAIR sequence to improve B1+ homogeneity and calculate the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit B1+ inhomogeneity present in a standard localizer scan, 3D AutoAlign localizers and SA2RAGE B1+ maps were acquired in eight volunteers. A convolutional neural network (CNN) was then trained to predict the B1+ profile from the localizers and scale factors for the pulse power for each slice were calculated. The ability to predict the B1+ profile as well as how the derived pulse scale factors affected the FLAIR inversion efficiency were assessed in transverse, sagittal, and coronal orientations. Results: The predicted B1+ maps matched the measured B1+ maps with a mean difference of 4.45% across all slices. The acquisition in the transverse orientation was shown to be most effective for this method and delivered a 40% reduction in SAR along with 1min and 30-sec reduction in scan time (28%) without degradation of image quality. Conclusion: We propose a SAR reduction technique based on the prediction of B1+ profiles from standard localizer scans using a CNN and show that scaling the inversion pulse power slice-by-slice for FLAIR sequences at 7T reduces SAR and scan time without compromising image quality.

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Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, Steffen Bollmann. 2019-11-19. Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks. https://doi.org/10.1002/mrm.28590

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