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

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.

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 1 min 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.