Malebogo Ngoepe

Vincent Milimo Masilokwa Punabantu, Malebogo Ngoepe, Amit Kumar Mishra et al.

Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.

Fabian Holzberger, Struan Hume, Markus Muhr et al.

Endovascular treatment of cerebral aneurysms aims to achieve functional occlusion and isolation of the aneurysm sac from bloodflow. In clinical practice, treatment success is assessed primarily through digital subtraction angiography (DSA), which visualizes contrast-agent inflow and washout but does not directly resolve thrombus formation driving early occlusion. We present a computational framework that couples acute fibrin thrombus formation with virtual angiography, enabling early thrombus growth to be interpreted through clinically familiar DSA-like imaging. Three common treatment strategies: endovascular coiling, flow diversion, and stent-assisted coiling, are modeled under pulsatile hemodynamics and linked to simulated contrast transport. Across three representative aneurysm morphologies, the simulations demonstrate that while devices reduce inflow, residual contrast access and trapping may persist, with early thrombus formation contributing substantially to perfusion suppression and altered washout patterns. These effects are clearly reflected in the virtual angiographic imaging. The importance of vortical structures in device-induced thrombosis is highligthed in one of the cases. By seeking to align modelling and simulation tools with clinically-relevant metrics, with a particular focus on occlusion outcome, this work presents a good starting point for bridging the gap between these two paradigms.