Accelerated Motion Correction for MRI using Score-Based Generative Models

Brett Levac, Ajil Jalal, Jon Tamir

Abstract:

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers
from long scan times which, aside from increasing operational costs, can lead to image artifacts due to
patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest
as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning
based reconstruction techniques have been proposed which decrease scan time by reducing the number of
measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used
to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution
shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using score-based generative
models. Our method does not make specific assumptions on the sampling trajectory or motion pattern
at training time and thus can be flexibly applied to various types of measurement models and patient
motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid
motion