UAG: Integrating R2UNet and Attention-Guided GNN for Robust Left Ventricle Motion Estimation

Background: Cardiac diseases significantly affect the structure and function of the left ventricle LV during the cardiac cycle. Purpose: Develop a robust framework UAG for precise detection and correspondence estimation of aberrant LV myocardial motion, enhancing di-agnostic accuracy in cardiac disease management. Methods: This paper proposes UAG, an innovative framework for LV motion estimation. The UAG framework integrates a U-shaped network ar-chitecture R2UNet for precise LV endocardial contour segmentation and a graph neural network GNN enhanced with attention mechanisms for robust feature matching. Initially, R2UNet is trained on cardiac magnetic resonance CMR images to extract discriminative features representing key points along the LV myocardial boundary. Subsequently, the GNN, combined with the Sinkhorn algorithm, establishes accurate correspondence between land-marks across diverse cardiac phases by leveraging both spatial and semantic feature relationships. Results: Performance evaluation on two publicly available cardiac da-tasets demonstrates UAG’s superiority over state-of-the-art methods. Using matching accuracy ACC and average perpendicular distance APD as evaluation metrics, UAG achieves the lowest ACC and APD values, outper-forming existing techniques in both normal and pathological LV contour scenarios. Conclusions: Experimental results validate UAG’s exceptional capability in LV motion estimation, particularly for images with abnormal contours. The integration of R2UNet’s multi-scale feature extraction and the attention-guided GNN ensures robustness against morphological variations, highlight-ing its potential for clinical applications in cardiac diagnostics.