MRzero - Automated discovery of MRI sequences using supervised learning

Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task -driven cost function this allows for an efficient exploration of novel MR sequence strategies. Methods: The scanning and reconstruction process is simulated end -to -end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T1 and 77,, and Bo. As a proof of concept, we use both conventional MR images and T1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time. Results: In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo. Conclusions: Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.

Automated summary

A supervised learning framework is proposed for automatically generating MR sequences and reconstruction based on target contrast, allowing for efficient exploration of novel strategies. The scanning and reconstruction process is simulated using RF and gradient moment events, and delay times, with optimization based on data fidelity, SAR penalty, and scan time. The approach demonstrates the potential for automated MR sequence generation through differentiable Bloch equation simulations and supervised learning.