Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning B1+ estimation

Abstract

Purpose: 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.

Methods: We used a time-resampled frequency-offset corrected inversion (TR-FOCI) adiabatic pulse for spin inversion in a T₂ -FLAIR sequence to improve $B_1^{+}$ homogeneity and calculated the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit $B_1^{+}$ inhomogeneity in a standard localizer scan, we acquired 3D AutoAlign localizers and SA2RAGE $B_1^{+}$ maps in 28 volunteers. Then, we trained a convolutional neural network (CNN) to estimate the $B_1^{+}$ profile from the localizers and calculated pulse scale factors for each slice. We assessed the predicted $B_1^{+}$ profiles and the effect of scaled pulse amplitudes on the FLAIR inversion efficiency in oblique transverse, sagittal, and coronal orientations.

Results: The predicted $B_1^{+}$ 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 1 min and 30 s reduction in SAR induced delay time while delivering identical image quality.

Conclusion: We propose a SAR reduction technique based on the estimation of $B_1^{+}$ 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.

Keywords: ${\mathrm{B}}_1^{+}$ profile; FLAIR; SA2RAGE; TR-FOCI; convolutional neural network (CNN); specific absorption rate (SAR).