Abstract
Abstract Numerical simulations are increasingly often involved in developing new and improving existing medical therapies. While the models involved in those simulations are designed to resemble a specific phenomenon realistically, the results of the interplay of those models are often not sufficiently validated. We created a plugin for a cardiac simulation framework to validate the simulation results using clinical MRI data. The MRI data were used to create a static wholeheart mesh as well as slices from the left ventricular short axis, providing the motion over time. The static heart was a starting point for a simulation of the heart’s motion. From the simulation result, we created slices and compared them to the clinical MRI slices using two different metrics: the area of the slices and the point distances. The comparison showed global similarities in the deformation of simulated and clinical data, but also indicated points for potential improvements. Performing this comparison with more clinical data could lead to personalized modeling of elastomechanics of the heart.
Highlights
The numerical simulation was performed on a whole-heart geometry consisting of 7623 points and 42282 tetrahedral volume elements of a heart in end-diastolic state.Despite a decrease in mortality of cardiovascular diseases, they are still the most common cause of death in Germany [1]
The area for all slices started decreasing until they reached their minimum in the peak systole at 500 ms
We introduced a metric-driven approach to validate the deformation of a simulation of the human heart based on clinical data
Summary
The numerical simulation was performed on a whole-heart geometry consisting of 7623 points and 42282 tetrahedral volume elements of a heart in end-diastolic state. Despite a decrease in mortality of cardiovascular diseases, they are still the most common cause of death in Germany [1]. Finding and improving therapies is a major goal of many researchers. Simulation frameworks support researchers and clinicians in developing and improving medical therapies but need to be verified and validated. Land et al conducted a verification study on cardiac mechanics simulation software in 2015. They, as well as others, defined verification as “determining how accurate a computer program solves the equations of a mathematical model” and validation is defined as “determining how well a mathematical model represents the real world phenomena it is intended to predict” [2]
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