Abstract
With heart and cardiovascular diseases continually challenging healthcare systems worldwide, translating basic research on cardiac (patho)physiology into clinical care is essential. Exacerbating this already extensive challenge is the complexity of the heart, relying on its hierarchical structure and function to maintain cardiovascular flow. Computational modelling has been proposed and actively pursued as a tool for accelerating research and translation. Allowing exploration of the relationships between physics, multiscale mechanisms and function, computational modelling provides a platform for improving our understanding of the heart. Further integration of experimental and clinical data through data assimilation and parameter estimation techniques is bringing computational models closer to use in routine clinical practice. This article reviews developments in computational cardiac modelling and how their integration with medical imaging data is providing new pathways for translational cardiac modelling.
Highlights
Heart function is the orchestration of multiple physical processes occurring across spatial scales that must act in concert to carry out its principal role: the transport of blood through the cardiovascular system
An alternative employed in some cardiac mechanics studies is the perturbed Lagrangian approach, whereby pressure and displacement are solved with the pressure–volume constitutive relation given as the required constraint [73,96,97]
Extension of fluid– structure interaction (FSI) techniques to study the interaction between blood flow and the ventricles was achieved by McQueen & Peskin [140,141], which was subsequently used for later studies of the heart [142,143,144]
Summary
Heart function is the orchestration of multiple physical processes occurring across spatial scales that must act in concert to carry out its principal role: the transport of blood through the cardiovascular system. Modern imaging is capable of recording the anatomy and motion of the heart, its tissue architecture, blood flow and perfusion, metabolism as well as numerous other pictures potentially useful in characterizing the state of a patient’s heart [31] With such a wealth of data, the challenge becomes integration and contextualization. After the challenges of data–model fusion are met and model analysis provides novel insight, the difficulty turns to translating these findings into clinically useable decision-making tools that can be robustly tested through clinical trials, proving their efficacy and superiority compared to existing techniques This effort is the principle aim of translational cardiac modelling (TCM), bringing cardiac modelling and model-based outcomes into the clinical routine. While this article focuses on translation of ventricular mechanics models in the heart, this discussion reviews tools and advancements that may facilitate other translational modelling efforts
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