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

Abstract

The purpose of this study is to demonstrate a method for specific absorption rate (SAR) reduction for 2D T2 -FLAIR MRI sequences at 7T by predicting the required adiabatic radiofrequency (RF) pulse power and scaling the RF amplitude in a slice-wise fashion. We used a time-resampled frequency-offset corrected inversion (TR-FOCI) adiabatic pulse for spin inversion in a T2 -FLAIR sequence to improve homogeneity and calculated the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit inhomogeneity in a standard localizer scan, we acquired 3D AutoAlign localizers and SA2RAGE maps in 28 volunteers. Then, we trained a convolutional neural network (CNN) to estimate the profile from the localizers and calculated pulse scale factors for each slice. We assessed the predicted profiles and the effect of scaled pulse amplitudes on the FLAIR inversion efficiency in oblique transverse, sagittal, and coronal orientations. The predicted amplitude maps matched the measured ones with a mean difference of 9.5% across all slices and participants. The slice-by-slice scaling of the TR-FOCI inversion pulse was most effective in oblique transverse orientation and resulted in a 1min and 30 s reduction in SAR induced delay time while delivering identical image quality. We propose a SAR reduction technique based on the estimation of 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.