Cardiac motion tracking with bidirectional latent neural ODEs from cine cardiac MRI

Accurate and temporally consistent cardiac motion tracking from cine MRI is essential for functional assessment and disease analysis. However, existing learning-based methods are typically formulated in discrete time and struggle to capture long-range temporal dependencies, often leading to accumulated errors and physically inconsistent motion. In this paper, we propose a unified unsupervised framework for cardiac motion tracking based on latent neural ordinary differential equations (ODEs). A frame-aware encoder extracts motion-sensitive features with temporal embeddings, which are evolved in a compact latent space via a neural ODE to model continuous-time deformation dynamics. To capture complex temporal variations in an efficient manner, we incorporate a conditional MLP-based dynamics module and further perform bidirectional forward-backward evolution within a unified framework. The evolved latent representations are decoded into inter-frame deformation fields, and a bidirectional Lagrangian regularization is introduced to enforce long-term temporal consistency and motion reversibility across the cardiac cycle. Extensive experiments on the ACDC and M&Ms datasets demonstrate that the proposed method achieves state-of-the-art performance, producing temporally consistent and physiologically plausible motion fields with a lightweight and efficient architecture.