Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction

TL;DR

This paper introduces a joint implicit neural representation framework for dynamic MRI that models both image sequences and motion fields simultaneously, improving reconstruction quality without prior flow estimation.

Contribution

It proposes a novel INR-based method that couples image and motion modeling via physics-inspired regularization, enhancing dynamic MRI reconstruction accuracy.

Findings

Outperforms state-of-the-art methods in reconstruction quality

Accurately estimates motion fields without pre-estimated flow

Achieves higher temporal fidelity in dynamic MRI

Abstract

Dynamic magnetic resonance imaging (dMRI) captures temporally-resolved anatomy but is often challenged by limited sampling and motion-induced artifacts. Conventional motion-compensated reconstructions typically rely on pre-estimated optical flow, which is inaccurate under undersampling and degrades reconstruction quality. In this work, we propose a novel implicit neural representation (INR) framework that jointly models both the dynamic image sequence and its underlying motion field. Specifically, one INR is employed to parameterize the spatiotemporal image content, while another INR represents the optical flow. The two are coupled via the optical flow equation, which serves as a physics-inspired regularization, in addition to a data consistency loss that enforces agreement with k-space measurements. This joint optimization enables simultaneous recovery of temporally coherent images and motion fields without requiring prior flow estimation. Experiments on dynamic cardiac MRI datasets demonstrate that the proposed method outperforms state-of-the-art motion-compensated and deep learning approaches, achieving superior reconstruction quality, accurate motion estimation, and improved temporal fidelity. These results highlight the potential of implicit joint modeling with flow-regularized constraints for advancing dMRI reconstruction.