Computer Aided Diagnostics and Intervention Planning in the Aortic Valve: An Application on Aortic Stenosis and Transcatheter Aortic Valve Replacement

Abstract

Aortic stenosis (AS) is a critical valvular disease often treated by Transcatheter Aortic Valve Replacement (TAVR). This thesis introduces several novel approaches for improving the assessment and management of AS and the associated TAVR procedure. The research presents new indices for characterizing AS progression, including the False Positive Rate (FPR) method for detecting and quantifying calcification in contrast-enhanced computed tomography (CT) images. This method adapts dynamically to the variability in calcium density and offers precise estimates of calcific burden. Additionally, a Minimal Variation Geometry Invariant Parametric Reconstruction (MVGIPR) method was developed to reconstruct the full geometry of the aortic valve complex (AVC). This approach enhances the accuracy of geometric models from routine CT scans, providing detailed 3D models of the aortic valve, including patient-specific anatomical and pathological features. Moreover, the Virtual Transcatheter Aortic Valve Replacement (VTAVR) framework is introduced for TAVR optimization and intervention planning using developments from both previous techniques. This novel simulation-based system incorporates kinematic modeling within a patient-specific parametric geometry to predict device deployment outcomes, including complications such as paravalvular leakage, patient-prosthesis mismatch, and left bundle branch block. By simulating patient-specific device deployment, the VTAVR framework may potentially enhance pre-procedural planning, leading to better surgical outcomes and reduced risks in TAVR procedures.