Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)

TL;DR

This paper presents an unsupervised, low-rank diffeomorphic flow model for motion-compensated 3D cardiac MRI, improving image reconstruction during free-breathing and ungated scans by learning from k-space data.

Contribution

It introduces a novel low-rank model for diffeomorphic motion fields, enabling improved free-breathing cardiac MRI reconstruction without supervision.

Findings

Enhanced image recovery compared to existing methods.

Effective modeling of complex cardiac motion.

Unsupervised learning from k-space data.

Abstract

We introduce an unsupervised motion-compensated image reconstruction algorithm for free-breathing and ungated 3D cardiac magnetic resonance imaging (MRI). We express the image volume corresponding to each specific motion phase as the deformation of a single static image template. The main contribution of the work is the low-rank model for the compact joint representation of the family of diffeomorphisms, parameterized by the motion phases. The diffeomorphism at a specific motion phase is obtained by integrating a parametric velocity field along a path connecting the reference template phase to the motion phase. The velocity field at different phases is represented using a low-rank model. The static template and the low-rank motion model parameters are learned directly from the k-space data in an unsupervised fashion. The more constrained motion model is observed to offer improved recovery compared to current motion-resolved and motion-compensated algorithms for free-breathing 3D cine MRI.