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

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 B1+ homogeneity and calculated the pulse power required for adiabaticity slice‐by‐slice to minimize the SAR. Drawing on the implicit B1+ inhomogeneity in a standard localizer scan, we acquired 3D AutoAlign localizers and SA2RAGE B1+ maps in 28 volunteers. Then, we trained a convolutional neural network (CNN) to estimate the B1+ profile from the localizers and calculated pulse scale factors for each slice. We assessed the predicted B1+ profiles and the effect of scaled pulse amplitudes on the FLAIR inversion efficiency in oblique transverse, sagittal, and coronal orientations. Results The predicted B1+ 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 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.