Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy

Figure 1.. Patient-specific workflow for predictive evaluation of TAVR. (a) Three-dimensional patient-specific models are constructed from CT images acquired during the standard clinical protocol for TAVR. (b) FSI-based evaluation protocol for assessing performance of TAVR against SAV and de-calcified normal AV. Also shown are the boundary conditions for flow: no-slip and no-penetration conditions are imposed at the wall. Fluid volume displaced by the moving interfaces (LV contraction and AV leaflet motion) are computed and applied as outflow at the aortic outlet [33]. Please note that aortic outlets were extended to avoid numerical instabilities (not shown). (c–h) Workflow for image-based fluid–structure interaction solver equipped with local adaptive mesh refinement and dynamic partition strategy.

Figure 2.. Clinical validation of model predictions with pre- and post-TAVR. (a) Comparison of maximum orifice from CT and model prediction for SAV; (b and c) model predictions of transvalvular pressure gradient and velocity pre- (SAV) and post-TAVR respectively, have a good agreement with (d–f) corresponding Doppler ultrasound data (three-chamber view) measured pre- and post-TAVR.

Figure 3.. Quantitative evaluation of leaflet displacement; (a) maximum AVA predicted in SAV, NAV and TAVR with contour colouring based on displacement in cm. (b) maximum leaflet tip displacement (cm) in time (ms) for the first 100 ms when deformation rate is the largest.

Figure 4.. Predictions of overall flow and AV dynamics for SAV (pre-TAVR) (left panel), NAV (middle panel) and post-TAVR (right panel) during (a) early, (b) peak and (c) late systole, show the degree of improvement achieved post-intervention and how it compares with normal AV dynamics.

Figure 5.. Predictions of wall shear stress (WSS) in Pa for (a) SAV (pre-TAVR), (b) NAV and (c) post-TAVR during peak systole. Posterior view shown with LV apex into the screen. Model predictions show increased WSS in the posterior walls of AAo for SAV, which is decreased post-TAVR.

Figure 6.. Predictions of vorticity for SAV (pre-TAVR) (left panel), NAV (middle panel) and post-TAVR (right panel) during (a) early, (b) peak and (c) late systole show vorticities peak during early systole and are quantitatively high for SAV while TAVR normalized vorticity in aorta.

Figure 7.. Predictions of viscous dissipation (energy loss) for SAV (pre-TAVR) (left panel), NAV (middle panel) and post-TAVR (right panel) during (a) early, (b) peak and (c) late systole show vorticities peak during early systole and are quantitatively high for SAV. Our model predictions show TAVR can potentially minimize energy loss in aorta.

Figure 8.. Model predictions of flow dynamics in ascending aorta near end systole (a) spatial distribution of velocity and valve configuration; (b) re-circulation behind AV leaflets with stream traces coloured based on vortex magnitudes; and (c) vorticity coloured based on its magnitude, of SAV, NAV and TAVR, respectively.