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

TL;DR

This study introduces a deep learning-based method to predict B1+ profiles from standard localizer scans, enabling adaptive scaling of adiabatic pulse power in 7T FLAIR MRI to significantly reduce SAR and scan time while maintaining image quality.

Contribution

The paper presents a novel CNN-based approach to predict B1+ maps from localizer scans, allowing slice-wise pulse power scaling for SAR reduction in 7T FLAIR MRI sequences.

Findings

Achieved a 40% SAR reduction without image quality loss.

Predicted B1+ maps matched measured maps with 4.45% mean difference.

Reduced scan time by 28% using the proposed method.

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.