A Left Ventricular Motion Estimation Method for CMR Cine Sequences Based on Shape-AttentionCascading
Wang Yu1, Sun Zheng2, Zhang Nan 1*
1(School of Biomedical Engineering, Capital Medical University, Beijing 100069, China) 2(Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China)
Abstract：Cardiac magnetic resonance cine (CMR cine) is a key non-invasive technique for assessing cardiac motion. However, its images often suffer from motion artifacts and improper imaging parameters, leading to blurring that compromises the boundary localization accuracy of existing left ventricular motion estimation methods, thereby affecting the reliability of cardiac function measurements. To address this issue, this study proposed a shape-attention cascade structure, consisting of a basic module and a sequence module, both of which was connected in cascade. The basic module enhanced boundary feature perception through a shape flow layer, enabling pixel-wise error correction of the initial prediction. The sequence module ensured motion estimation coherence using a bidirectional motion attention layer. In addition, this study introduced a novel data augmentation method targeting boundary noise and designs a weighted Dice loss function that integrates pixel confidence and frame confidence. Experimental results demonstratd that the proposed method significantly improved the estimation accuracy for the left ventricular motion. On the ACDC dataset, the Hausdorff distance of the proposed method is 3.83 mm, outperforming the baseline model at 7.51 mm; on the private dataset, the proposed method achieves 4.39 mm, outperforming the baseline model at 8.68 mm; on EchoNet-Dynamic, the proposed method achieves 4.86 mm, outperforming the baseline method at 5.87 mm. The above differences were all statistically significant as assessed by the Kruskal-Wallis test(P<0.05). This study provides an effective method for accurate cardiac motion estimation, with positive implications for advancing the clinical translation of automated cardiac function assessment.
王宇, 孙峥, 张楠. 基于形状注意力级联的CMR cine序列左心室运动估计方法[J]. 中国生物医学工程学报, 2026, 45(3): 267-275.
Wang Yu, Sun Zheng, Zhang Nan ,. A Left Ventricular Motion Estimation Method for CMR Cine Sequences Based on Shape-AttentionCascading. Chinese Journal of Biomedical Engineering, 2026, 45(3): 267-275.
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