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Updated: Jan 19, 2026

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
Published on: May 24, 2021
Unlocking 2D/3D+T myocardial mechanics from cine MRI: a mechanically regularized space-time finite element
Haizhou Liu1, Xueling Qin2, Zhou Liu3
1Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China; The Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, China.
None:
Accurate and biomechanically consistent quantification of cardiac motion remains a major challenge in cine MRI analysis. While classical feature-tracking and recent deep learning methods have improved frame-wise strain estimation, they often lack biomechanical interpretability and temporal coherence. In this study, we propose a spacetime-regularized finite-element digital image/volume correlation (FE-DIC/DVC) framework that enables 2D/3D+T myocardial motion tracking and strain analysis using only routine cine MRI. The method unifies Multiview alignment and 2D/3D+T motion estimation into a coherent pipeline, combining region-specific biomechanical regularization with data-driven based temporal decomposition to promote spatial fidelity and temporal consistency. A correlation-based Multiview alignment module further enhances anatomical consistency across short- and long-axis views. We evaluate the approach on one synthetic dataset (with ground-truth motion and strain fields), three public datasets (with ground-truth landmarks or myocardial masks), and a clinical dataset (with ground-truth myocardial masks). 2D+T motion and strain are evaluated across all datasets, whereas Multiview alignment and 3D+T motion estimation is assessed only on the clinical dataset. Compared with two classical feature-tracking methods and four state-of-the-art deep-learning baselines, the proposed method improves 2D+T motion and strain estimation accuracy as well as temporal consistency on the synthetic data, achieving a displacement RMSE of 0.35 pixels (vs. 0.73 pixels), an equivalent-strain RMSE of 0.05 (vs. 0.097), and a temporal consistency of 0.97 (vs. 0.91). On public and clinical data, it achieves superior performance in terms of a landmark error of 1.96 mm (vs. 3.15 mm), a boundary-tracking Dice of 0.80-0.87 (a 2-4% improvement over the best-performing baseline), and overall registration quality that consistently ranks among the top two methods. By leveraging only standard cine MRI, this work enables 2D/3D+T myocardial mechanics and provides a practical route toward 4D cardiac function assessment.
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