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Atrial fibrillation (AF) carries out a 5-fold increase in stroke risk, related to embolization of thrombi clotting in left atrium (LA). Left atrial appendage (LAA) is the site with the highest blood stasis which causes thrombus formation. About 90 % of the intracardiac thrombi in patients with cardioembolic events originally develop in the LAA. Recent studies have been focused on the association between LAA anatomical features and stroke risk and provided conflicting results. Haemodynamic and fluid dynamic information on the LA and mostly on the LAA may improve stroke risk stratification. Therefore, the aim of this study was the design and development of a workflow to quantitatively define the influence of the LAA morphology on LA hemodynamics. Five 3D LA anatomical models, obtained from real clinical data, which were clearly different as regard to LAA morphology were used. For each LAA we identified and computed several parameters describing its geometry. Then, one LA chamber model was chosen and a framework was developed to connect the different LAAs belonging to the other four patients to this model. These new anatomical models represented the computational domain for the computational fluid dynamics (CFD) study; simulations of the hemodynamics within the LA and LAA were performed in order to evaluate the interplay of the LAA shape on the blood flow characteristics in AF condition. CFD simulations were carried out for five cardiac cycles. Blood velocity, vorticity, LAA orifice velocity, residence time computed in the five models were compared and correlated with LAA morphologies. Results showed that not only complex morphologies were characterized by low velocities, low vorticity and consequently could carry a higher thrombogenic risk; even qualitatively simple morphologies showed a thrombogenic risk equal, or even higher, than more complex auricles. CFD results supported the hypothesis that LAA geometric characteristics plays a key-role in defining thromboembolic risk. LAA geometric parameters could be considered, coupled with the morphological characteristics, for a comprehensive evaluation of the regional blood stasis. The proposed procedure might address the development of a tool for patient-specific stroke risk assessment and preventive treatment in AF patients, relying on morpho-functional defintion of each LAA type.

In this paper, we propose a full computational framework to simulate the hemodynamics in the aorta including the valve. Closed and open valve surfaces, as well as the lumen aorta, are reconstructed directly from medical images using new ad hoc algorithms, allowing a patient-specific simulation. The fluid dynamics problem that accounts from the movement of the valve is solved by a new 3D–0D fluid–structure interaction model in which the valve surface is implicitly represented through level set functions, yielding, in the Navier–Stokes equations, a resistive penalization term enforcing the blood to adhere to the valve leaflets. The dynamics of the valve between its closed and open position is modeled using a reduced geometric 0D model. At the discrete level, a finite element formulation is used and the SUPG stabilization is extended to include the resistive term in the Navier–Stokes equations. Then, after time discretization, the 3D fluid and 0D valve models are coupled through a staggered approach. This computational framework, applied to a patient-specific geometry and data, allows to simulate the movement of the valve, the sharp pressure jump occurring across the leaflets, and the blood flow pattern inside the aorta

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<p style='text-indent:20px;'>We present a new computational model for the numerical simulation of blood flow in the human left heart. To this aim, we use the Navier-Stokes equations in an Arbitrary Lagrangian Eulerian formulation to account for the endocardium motion and we model the cardiac valves by means of the Resistive Immersed Implicit Surface method. To impose a physiological displacement of the domain boundary, we use a 3D cardiac electromechanical model of the left ventricle coupled to a lumped-parameter (0D) closed-loop model of the remaining circulation. We thus obtain a one-way coupled electromechanics-fluid dynamics model in the left ventricle. To extend the left ventricle motion to the endocardium of the left atrium and to that of the ascending aorta, we introduce a preprocessing procedure according to which an harmonic extension of the left ventricle displacement is combined with the motion of the left atrium based on the 0D model. To better match the 3D cardiac fluid flow with the external blood circulation, we couple the 3D Navier-Stokes equations to the 0D circulation model, obtaining a multiscale coupled 3D-0D fluid dynamics model that we solve via a segregated numerical scheme. We carry out numerical simulations for a healthy left heart and we validate our model by showing that meaningful hemodynamic indicators are correctly reproduced.</p>

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In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.