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

2025-07-23

AI Summary

Existing myocardial motion tracking methods predominantly rely on pairwise single-frame image registration, neglecting the temporal continuity of cardiac motion—resulting in spatially and temporally inconsistent, non-smooth displacement field estimates. To address this, we propose the first continuous motion tracking framework specifically designed for cardiac MRI sequences. Our method introduces three key innovations: (1) a bidirectional Mamba module with bidirectional scanning to efficiently capture long-range temporal dependencies; (2) a sequence-aware motion decoder that fuses motion cues from neighboring frames to enhance spatiotemporal consistency; and (3) a lightweight structured state-space model enabling high-accuracy deformation field estimation without increasing computational overhead. Evaluated on two public MRI datasets, our approach significantly outperforms conventional and state-of-the-art methods in accuracy, smoothness, and temporal coherence. The source code is publicly available.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Motion and Path Planning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Tracking cardiac motion smoothly from sequential MRI images
• Overcoming inconsistent motion estimation in existing methods
• Enhancing temporal coherence in myocardial dynamics modeling

Innovation

• Mamba-based network for sequential image tracking
• Bi-directional Mamba block for smooth deformation
• Motion decoder enhances temporal coherence