Patient-Specific Virtual TAVI Assessment for the Quantification of Paravalvular Leakage
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Transcatheter aortic valve implantation (TAVI) is an established treatment for aortic stenosis, yet paravalvular leakage (PVL) remains a clinically relevant complication. Patient-specific numerical simulations offer powerful tools for predicting procedural outcomes. However, current models are limited by methodological heterogeneity and incomplete validation against clinical data. To fill this gap, this study aims to validate different numerical strategies for PVL prediction by quantitively comparing in-silico outputs with post-TAVI MRI measurements, to identify the modelling approach with the highest clinical accuracy. Ten patients undergoing TAVI were prospectively enrolled, and dedicated post-procedural MRI images were acquired. Patient-specific anatomies were reconstructed from pre-operative CTs, and device models were calibrated using ad-hoc crimping tests. FEA simulations reproduced device deployment within each native anatomy. Subsequently, diastolic CFD simulations were performed using patient-specific transvalvular pressure gradients as boundary conditions (BCs). A novel FSI framework was also implemented, setting intraoperatively acquired pressure waveforms as BCs, with calibrated Windkessel models at coronary outlets. For each strategy (FEA, CFD, FSI), the regurgitant fraction (RF) was quantified, and its absolute deviation from MRI-derived RF was computed. Figure1a depicts the workflow. Simulations were successfully completed for all patients. The directionality of systolic flow within the aortic root exhibited good qualitative agreement with MRI acquisitions (Figure1b). The average absolute RF deviation with respect to MRI-based measurements was 7.73±5.63% for FEA-based geometric assessment, 4.04±2.81% for CFD and 2.36±1.96% for FSI analysis. Among the investigated numerical strategies, FSI demonstrated the highest accuracy in predicting post-TAVI PVL. These findings represent a methodological advancement over current state-of-the-art, identifying the validated FSI simulations developed in this study as an accurate in-silico strategy for assessing TAVI outcomes.
