ArticleComputers in biology and medicine2024
In-silico heart model phantom to validate cardiac strain imaging.
Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Automated cardiac strain estimation from short-axis DENSE and Cine MR images.Biomechanics and modeling in mechanobiology · 2026Article
- Computational modeling of left ventricular flow using PC-CMR-derived four-dimensional wall motion.International journal of engineering science · 2026Article
- Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling.Advanced healthcare materials · 2026Review
- Comparison of noninvasive pressure-volume loops derived from cardiac magnetic resonance and transthoracic echocardiography in normal subjects.Scientific reports · 2026Article
- Advancing cardiac motion estimation with emerging AI techniques for enhanced echocardiographic image registration.MethodsX · 2025Article
- Complete spatiotemporal quantification of cardiac motion in mice through multi-view magnetic resonance imaging and super-resolution reconstruction.Scientific reports · 2025Article
- Non-Invasive Diagnosis of Chronic Myocardial Infarction via Composite In-Silico-Human Data Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement.Annals of biomedical engineering · 2025Article
- Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.Research square · 2024Article
- Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.bioRxiv : the preprint server for biology · 2024Article
- In-silico CT lung phantom generated from finite-element mesh.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
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10 authors.
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Abstract
The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.
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