Advanced intervention planning for Transcatheter Aortic Valve Implantations (TAVI) from CT using volumetric models

Aortic valve stenosis is a serious heart disease affecting a large group of elderly people. Recently minimal invasive procedures, such as the Transcatheter Aortic Valve Implantation (TAVI), are beginning to substitute conventional surgical techniques. Current methods [1] can extract basic biomarkers for TAVI such as optimal C-arm angulations, area and diameter measurements. However as the most prevalent TAVI complications (stroke and paravalvular leakages) are correlated with calcium and leaflet interactions within the valve a more advanced solution is needed. We propose a fully integrated system to extract automatically the patient specific model of the aortic valve including the volumetric model of the aortic valve leaflets and calcium from high resolution single phase CT. Based on the volumetric model advanced clinical parameters can be derived and used for e.g. patient selection, paravalvular leakage prediction and patient stroke risk assessment. We employ robust machine learning algorithms to estimate the valve model parameters. A multi-class classification method is introduced to label regions of calcium, leaflet and blood pool within the aortic valve and extract volumetric models of the aortic valve leaflets. Extensive quantitative and qualitative experiments on 198 volumetric data sets demonstrate an accurate DICE similarity score, i.e. 0.7 for the aortic valve leaflets and 0:86 for calcium tissue. Within 6 seconds a complete patient-specific model of the aortic valve can be estimated.