Data Consistent Deep Rigid MRI Motion Correction

Nalini M Singh, Neel Dey, Malte Hoffmann, Bruce Fischl, Elfar Adalsteinsson, Robert Frost, Adrian V Dalca, Polina Golland

Keywords: Motion Correction, MRI, Deep Learning, Hypernetworks, Data Consistency

Abstract: Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone. Our network produces a reconstruction as a function of two inputs: corrupted k-space data and motion parameters. We train the network using simulated, motion-corrupted k-space data generated from known motion parameters. At test-time, we estimate unknown motion parameters by minimizing a data consistency loss between the motion parameters, the network-based image reconstruction given those parameters, and the acquired measurements. Intra-slice motion correction experiments on simulated and realistic 2D fast spin echo brain MRI achieve high reconstruction fidelity while retaining the benefits of explicit data consistency-based optimization.

TL;DR: Explicitly enforcing data consistency in deep learning-based rigid motion correction yields high fidelity reconstructions and rapid optimization of motion parameters.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2301.10365/code)