MRzero - Automated discovery of MRI sequences using supervised learning

Abstract

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, $T_1$ and $T_2$ , and $\Delta B_0$ . As a proof of concept, we use both conventional MR images and $T_1$ 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.

Keywords: AUTOSEQ; MR simulation; automatic MR; differentiable Bloch equation; machine learning.