MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI

TL;DR

This paper introduces MCM, a novel cardiac motion tracking network that leverages sequential MRI images and a bi-directional Mamba block to achieve smooth, consistent, and continuous myocardial motion estimation, outperforming existing methods.

Contribution

The paper presents a new Mamba-based network that explicitly models continuous cardiac motion from sequential images, improving temporal coherence and estimation accuracy.

Findings

Outperforms state-of-the-art methods on public datasets

Achieves smoother and more consistent motion tracking

Effectively learns continuous myocardial dynamics without extra computational cost

Abstract

Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases, for which cine cardiac magnetic resonance (CMR) has been established as the gold standard imaging modality. Many existing methods learn motion from single image pairs consisting of a reference frame and a randomly selected target frame from the cardiac cycle. However, these methods overlook the continuous nature of cardiac motion and often yield inconsistent and non-smooth motion estimations. In this work, we propose a novel Mamba-based cardiac motion tracking network (MCM) that explicitly incorporates target image sequence from the cardiac cycle to achieve smooth and temporally consistent motion tracking. By developing a bi-directional Mamba block equipped with a bi-directional scanning mechanism, our method facilitates the estimation of plausible deformation fields. With our proposed motion decoder that integrates motion information from frames adjacent to the target frame, our method further enhances temporal coherence. Moreover, by taking advantage of Mamba's structured state-space formulation, the proposed method learns the continuous dynamics of the myocardium from sequential images without increasing computational complexity. We evaluate the proposed method on two public datasets. The experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms both conventional and state-of-the-art learning-based cardiac motion tracking methods. The code is available at https://github.com/yjh-0104/MCM.