The Karlsruhe Heart Model KaHMo: a modular framework for numerical simulation of cardiac hemodynamics

Numerical methods are rapidly gaining importance for answering medical questions. One field in which these answer are especially valuable is cardiology. The understanding of the cardiac function on a detailed, physical level can help to improve diagnostics, prognosis and therapy for a large number of pathologies. The KaHMo (Karlsruhe Heart Model) is developed as a framework for the patient specific numerical simulation of the intraventricular flow. The framework combines different methods from several disciplines. As a means to simulate the cardiac flow in a given patient specific heart, KaHMo MRI derives the time dependent geometry of the endocardium and performs a numerical simulation of the intraventricular flow. In order to be able to predict the influence of pathological changes in e.g. the myocardium or the valves on the contraction of the heart and the flow driven by this movement, KaHMo FSI employs special Fluid-Structure-Interaction methods and a composite approach to muscular dynamics to simulate the complex interaction of non linear elastomechanics with hemodynamics. The framework is supported by additional models which include a model of the human circulatory system to derive the systemic pressure response, rheological models for the non- Newtonian behaviour of the blood as well as models for prediction of hemolysis and thrombosis risks in artificial blood pumps or ventricular assist devices. Future developments may incorporate electrodynamical models to include the possibility to predict the effect of e.g. arrythmia or therapeutical ablation on the heart function. The vision is a macroscopic holistic model of the human heart that can help to answer the ever pressing what if? questions

Using high resolution cardiac CT data to model and visualize patient-specific interactions between trabeculae and blood flow

Abstract. In this paper, we present a method to simulate and visualize blood flow through the human heart, using the reconstructed 4D motion of the endocardial surface of the left ventricle as boundary conditions. The reconstruction captures the motion of the full 3D surfaces of the complex features, such as the papillary muscles and the ventricular trabeculae. We use visualizations of the flow field to view the interactions between the blood and the trabeculae in far more detail than has been achieved previously, which promises to give a better understanding of cardiac flow. Finally, we use our simulation results to compare the blood flow within one healthy heart and two diseased hearts.

Aortic relative pressure components derived from four-dimensional flow cardiovascular magnetic resonance.

PURPOSE: To describe the assessment of the spatiotemporal distribution of relative aortic pressure quantifying the magnitude of its three major components. METHODS: Nine healthy volunteers and three patients with aortic disease (bicuspid aortic valve, dissection, and Marfan syndrome) underwent 4D-flow CMR. Spatiotemporal pressure maps were computed from the CMR flow fields solving the pressure Poisson equation. The individual components of pressure were separated into time-varying inertial ("transient"), spatially varying inertial ("convective"), and viscous components. RESULTS: Relative aortic pressure is primarily caused by transient effects followed by the convective and small viscous contributions (64.5, 13.6, and 0.3 mmHg/m, respectively, in healthy subjects), although regional analysis revealed prevalent convective effects in specific contexts, e.g., Sinus of Valsalva and aortic arch at instants of peak velocity. Patients showed differences in peak transient values and duration, and localized abrupt convective changes explained by abnormalities in aortic geometry, including the presence of an aneurysm, a pseudo-coarctation, the inlet of a dissection, or by complex flow patterns. CONCLUSION: The evaluation of the three components of relative pressure enables the quantification of mechanistic information for understanding and stratifying aortic disease, with potential future implications for guiding therapy