Fluid-Structure-Interaction Modelling of the Heart Hemodynamics using Statistical Shape Models

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Analysis of cardiac hemodynamics plays an increasing role in diagnosis and treatment of heart diseases. A routine assessment of complex parameters proposed as hemodynamic biomarkers for cardiovascular diseases is currently difficult. However, numerical modeling allows calculation of spatially and temporally resolved information of patient-specific heart function. Various modelling approaches are proposed for analysis of heart function, ranging from simple 0D Lumped Element Models (LEM) to very complex 4D models coupling electro-, structural, and fluid mechanics. The final aim of this project is to allow patient-specific simulations under clinical conditions and thus support physicians during clinical routine. The proposed numerical approach will combine computational fluid dynamics (CFD) and statistical shape models (SSM). SSM allow the description of patient-specific geometries as a superposition of weighted shape modes. It is therefore possible to create a standardized simulation of the left ventricle based on an average shape, which is then personalized by adjusting mesh nodes. Different imaging modalities, namely CT, MRI and echocardiography, are used to parameterize the simulation. The ventricular contraction is modelled using a prescribed motion, meaning the contraction is acquired from image data. Although the shape of the opened aortic and mitral valve leaflets is also described by a shape model, their motion is calculated by means of mechanical finite element simulations, since a time-resolved segmentation of leaflets is often not possible or associated with high uncertainty. Furthermore, the boundary conditions describing pre- and afterload, specifically at the pulmonary veins and aorta, are provided by a personalized LEM. The proposed CFD-SSM numerical approach is flexible and allows identification of an optimal model complexity: simple enough to be applicable in a clinical setting and complex enough to provide required level of information for precise diagnosis and treatment decision. The approach should enable robust and fast simulation of cardiac hemodynamics for clinicians. It has the potential to allow complete automation of patient-specific modelling. Automation will improve usability and minimize all user-related errors. The initial clinical focus of the project are heart valve diseases. The usability of the approach will be shown using in-vitro experiments using MRI for an analysis of hemodynamics in phantom models. The successful realization of the project requires close cooperation between clinical and engineering scientists, which is well established at the Institute of Cardiovascular Computer-assisted Medicine at Charité – Universitätsmedizin Berlin.