No Strings Attached: Predicting Tricuspid Valve Deformation Without In Vivo Chordal Geometry
Predictive biomechanical models of the tricuspid valve require accurate representation of the chordae tendineae, yet subject-specific chordal geometry is difficult to reconstruct from non-invasive imaging. Here, we adopt a framework for generating functionally equivalent synthetic chordae without prior knowledge of in vivo chordal attachments. To this end, we first adapt an anatomy-informed…
Predicting tricuspid valve deformation without in vivo chordal geometry is possible through a new predictive biomechanical model. This model uses synthetic chordae to reproduce the valve's closure accurately. An anatomy-informed hyperelastic shape-matching method establishes correspondence between end-diastolic and end-systolic leaflet configurations.
Synthetic chordal insertions are generated using zone-based rejection sampling and calibrated by reaction forces and chordal stress-stretch relationships. The framework validates against Texas TriValve 1.1, a high-fidelity finite element model of a human tricuspid valve. Shape matching accurately reproduces end-systolic geometry with minimal distance errors.
Increasing synthetic chordal insertions reduces contact area errors and maintains low maximum principal stretch and areal strain errors. This approach demonstrates the potential for creating synthetic subvalvular anatomy in predictive tricuspid valve models using imaging-derived data, without needing explicit subject-specific chordal geometry.
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