Simon Mendez

To evaluate the impact of intracardiac flow features on the efficiency of the transfer of the myocardium's contractile mechanical power into blood flow hydraulic power. A time evolving 3D mesh corresponding to a patient‐specific left heart was generated from medical images to obtain the intracardiac hemodynamics via computational fluid dynamics. A parametric model of the mitral valve is used to investigate the influence of its opening area and orientation on the hemodynamics. The complete ventricular kinetic energy balance equation is established, and its terms are quantified to characterize the fraction of contractile power transferred to the blood flow. The simulations indicate that the influence of MV on blood flow pattern and vortices is significant. Dissipation and TKE in the left ventricle have a strong dependence on the MV model. Their values are higher when mimicking pathologies such as calcification of the MV (resulting in a smaller opening area), and reduced when the opening area is larger. While the orientation of the MV opening exerts a considerable impact on flow patterns, its effect on dissipation and TKE is less pronounced. For all the cases considered, the amount of energy dissipated over one cardiac cycle is a small fraction (1%–2%) of the energy at play to pump blood towards the aorta. In absence of valve leakage, the energy dissipated by viscous effect is marginal in all the healthy and pathological cases considered.

Purpose: The aim of this study is to investigate the impact of the partial echo on 4D flow MRI sequences thanks to in silico coupled MRI‐CFD (computational fluid dynamics) simulations. Methods: Two sequences are studied: one with a full echo (FE) and another using partial echo (PE) with an echo symmetry fraction of 0.75. MRI‐CFD simulations are conducted on an in silico pulsatile flow phantom for a sinusoidal inflow and a physiological inflow typical of the ascending aorta. Results: For both inflow signals, PE‐based simulations exhibited better compliance with their matching CFD simulations compared to the FE ones. Conclusion: The reduction of flow misregistration artifacts achieved through the use of PE appears to be more beneficial than the drawback of incomplete k‐space filling. The MRI‐CFD framework presented in this study appears as a useful tool to investigate the design of MRI sequences and to stratify its different sources of errors.

Motivated by the study of the electrodynamics of particles, we propose in this work an arbitrary-order discrete de Rham scheme for the treatment of elliptic problems with potential and flux jumps across a fixed interface. The scheme seamlessly supports general elements resulting from the cutting of a background mesh along the interface. Interface conditions are enforced weakly à la Nitsche. We provide a rigorous convergence of analysis of the scheme for a steady model problem and showcase an application to a physical problem inspired by the Leaky Dielectric Model.

Motivated by the study of the electrodynamics of particles, we propose in this work an arbitrary-order discrete de Rham scheme for the treatment of elliptic problems with potential and flux jumps across a fixed interface. The scheme seamlessly supports general elements resulting from the cutting of a background mesh along the interface. Interface conditions are enforced weakly à la Nitsche. We provide a rigorous convergence of analysis of the scheme for a steady model problem and showcase an application to a physical problem inspired by the Leaky Dielectric Model.

We study viral transmission in crowds via the short-ranged airborne pathway using a purely model-based approach. Our goal is two-pronged. Firstly, we illustrate with a concrete and pedagogical case study how to estimate the risks of new viral infections by coupling pedestrian simulations with the transmission algorithm that we recently released as open-source code. The algorithm hinges on pre-computed viral concentration maps derived from computational fluid dynamics (CFD) simulations. Secondly, we investigate to what extent the transmission risk predictions depend on the pedestrian dynamics model in use. For the simple bidirectional flow under consideration, the predictions are found to be surprisingly stable across initial conditions and models, despite the different microscopic arrangements of the simulated crowd, as long as the crowd evolves in a qualitatively similarly way. On the other hand, when major changes are observed in the crowd's behaviour, notably whenever a jam occurs at the centre of the channel, the estimated risks surge drastically.

Intraventricular vector flow mapping ( i VFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional i VFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original i VFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow