Image-Based Predictive Modeling of Heart Mechanics
Abstract
Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.
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Given knowledge of the boundary loading, and imaged geometry and displacements, finite element analysis can estimate material properties compatible with the imaged deformation.
“Given knowledge of the boundary loading, and imaged geometry and displacements, finite element analysis can estimate material properties compatible with the imaged deformation [147].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [147]
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- Authors
- Wang, V. Y., Nielsen, P. M. F., & Nash, M. P.
- Affiliations
- Auckland Bioengineering Institute and
- University of Auckland (NZ)
- Venue
- Annual Review of Biomedical Engineering
- Publisher
- Annual Reviews
- Published
- 2015-12-07
- Volume
- 17
- Issue
- 1
- Pages
- 351-383
- ISSN
- 1523-9829 (ISSN-L)
- 1545-4274
- OpenAlex
- W2159293185
- Cited by
- 63
- References
- 0
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 6 · 2022: 3 · 2021: 13 · 2020: 14 · 2019: 3 · 2018: 2 · 2017: 8 · 2016: 3
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
- Keywords
- cardiac mechanics, image-based modeling, cardiac imaging modalities, myocardial function, 3D cardiac imaging, computational cardiac modeling, surgical guidance, treatment planning, clinical translation
- MeSH terms
- Animals, Biomechanical Phenomena, Biomedical Engineering, Heart, Hemodynamics, Humans, Magnetic Resonance Imaging, Models, Cardiovascular, Myocardial Contraction, Myocardium, Tomography, X-Ray Computed, Imaging, Three-Dimensional, Biophysical Phenomena, Diffusion Tensor Imaging
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