Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts

TL;DR

This work tackles the challenge of selecting the optimal transcatheter pulmonary valve for patients with Tetralogy of Fallot by delivering an open-source, end-to-end pipeline that simulates TPVR in image-derived, patient-specific RVOT geometries. The authors combine ML-based RVOT segmentation, a custom SlicerHeart module for device placement, and FEBio-based finite-element simulations to quantify device–vessel interactions, including strain, stress, and contact areas. Key contributions include a rapid segmentation step, interactive TPV positioning, and quantitative metrics that reveal high stress/strain at the proximal and distal RVOT ends, highlighting the need for patient-specific device selection. The framework demonstrated on three pediatric anatomies demonstrates feasibility and potential clinical impact for preprocedural planning and device matching, with open avenues for incorporating hemodynamics and broader validation.

Abstract

Background - Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated open-source workflow for simulation of TPVR in image-derived models to inform device selection. Methods - Machine learning-based segmentation of CT scans was implemented to define the right ventricular outflow tract (RVOT). A custom workflow for device positioning and pre-compression was implemented in SlicerHeart. Resulting geometries were exported to FEBio for simulation. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Results - RVOT model creation and device placement could be completed in under 1 minute. Virtual device placement using FE simulations visually mimicked actual device placement and allowed quantification of vessel strain, stress, and contact area. Regions of higher strain and stress were observed at the proximal and distal end locations of the TPVs where the devices impinge the RVOT wall. No other consistent trends were observed across simulations. The observed variability in mechanical metrics across RVOTS, stents, and locations in the RVOT highlights that no single device performs optimally in all anatomies, thereby reinforcing the need for simulation-based patient-specific assessment. Conclusions - This study demonstrates the feasibility of a novel open-source workflow for the rapid simulation of TPVR which with further refinement may inform assessment of patient candidacy and optimal device selection.

Figures (7)

• Figure 1: Overview of Modeling Pipeline
• Figure 2: A) Shell models of our 3 patient-specific RVOT vessels, to scale. The example vessels were chosen to demonstrate workflow applicability in a range of RVOT sizes and shapes. RVOT morphopolgy types are determined as per schievano_variations_2007 B) TPV device models available in the TPVR Simulation module within SlicerHeart.
• Figure 3: Device placement and TPVR simulation module functionality.
• Figure 4: FEM modeling – staged deployment in FEBio.
• Figure 5: A) 1st principal Lagrangian strain distribution in simulated RVOT and TPV cases, and B) peak strain values observed in the RVOT across simulated cases, separated by proximal and distal halves.