MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T -induced blurring in spin echo sequences.

Purpose: An end-to-end differentiable 2D Bloch simulation is used to reduce T induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network.

Methods: Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard.

Results: The optimized RARE was able to successfully lower T -induced blurring for single-shot RARE sequences in proton density-weighted and T -weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance.

Conclusions: This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T -induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.