arXiv subjects

Fabian Holzberger

Publications and source records attributed to Fabian Holzberger.

6 recordsLinked to original sources

arXiv2026

Effective permeabilities for flow through anisotropic microscopic geometries

This work develops a computational and theoretical framework for determining effective permeabilities in anisotropic microscopic geometries containing dense, fibre-like obstacles, motivated by the need to model flow in coiled aneurysm domains accurately. Building on homogenisation theory and fully resolved simulations in Representative Elementary Volumes (REVs), we validate the permeability model introduced in [C. Boutin, Study of permeability by periodic and self-consistent homogenisation. Eur. J. Mech. A Solids, 19(4):603-632, 2000] and propose a systematic methodology for capturing the directional variations induced by fibre orientation. The resulting permeability tensors are incorporated into macroscopic flow simulations based on the Darcy equation, enabling direct comparison of anisotropic and isotropic permeability models across several benchmark configurations. Our findings show that anisotropy has a significant impact on local flow direction and magnitude, generating directional permeability contrasts which cannot be reproduced by classical isotropic approximations. By integrating coil-induced microstructural effects into continuum-scale hemodynamic models, the proposed approach enables more realistic assessment of post-treatment aneurysm flow behaviour. Beyond this clinical application, the framework is broadly applicable to other biomedical and engineering systems involving fibrous or filamentous porous microstructures.

physics.flu-dyn↗

arXiv2026

Device-Induced Thrombus Formation in Cerebral Aneurysms: Linking Patient-Specific Clot Modeling and Functional Occlusion to Virtual Angiographic Assessment

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.

cs.CE↗

arXiv2025

U-net based prediction of cerebrospinal fluid distribution and ventricular reflux grading

Previous work indicates evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increase at its peak after 24 hours. Performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings show that training with imaging data from only the first two hours post-injection yields tracer flow predictions comparable to models trained with additional later-stage scans. Validation against ventricular reflux gradings from neuroradiologists confirmed alignment with expert evaluations. These results demonstrate that deep learning-based methods for CSF flow prediction deserve more attention, as minimizing MR imaging without compromising clinical analysis could enhance efficiency, improve patient well-being, and lower healthcare costs.

eess.IV↗

arXiv2024

Numerical simulation of individual coil placement -- A proof-of-concept study for the prediction of recurrence after aneurysm coiling

Rupture of intracranial aneurysms results in severe subarachnoidal hemorrhage, which is associated with high morbidity and mortality. Neurointerventional occlusion of the aneurysm through coiling has evolved to a therapeutical standard. The choice of the specific coil has an important influence on secondary regrowth requiring retreatment. Aneurysm occlusion was simulated either through virtual implantation of a preshaped 3D coil or with a porous media approach. In this study, we used a recently developed numerical approach to simulate aneurysm shapes in specific challenging aneurysm anatomies and correlated these with aneurysm recurrence 6 months after treatment. The simulation showed a great variety of coil shapes depending on the variability in possible microcatheter positions. Aneurysms with a later recurrence showed a tendency for more successful coiling attempts. Results revealed further trends suggesting lower simulated packing densities in aneurysms with reoccurrence. Simulated packing densities did not correlate with those calculated by conventional software, indicating the potential for our approach to offer additional predictive value. Our study, therefore, pioneers a comprehensive numerical model for simulating aneurysm coiling, providing insights into individualized treatment strategies and outcome prediction. Future directions involve expanding the model's capabilities to simulate intraprocedural outcomes and long-term predictions, aiming to refine occlusion quality criteria and validate prediction parameters in larger patient cohorts. This simulation framework holds promise for enhancing clinical decision-making and optimizing patient outcomes in endovascular aneurysm treatment.

cs.CE↗

arXiv2024

Numerical simulation of endovascular treatment options for cerebral aneurysms

Predicting the long-term success of endovascular interventions in the clinical management of cerebral aneurysms requires detailed insight into the patient-specific physiological conditions. In this work, we not only propose numerical representations of endovascular medical devices such as coils, flow diverters or Woven EndoBridge but also outline numerical models for the prediction of blood flow patterns in the aneurysm cavity right after a surgical intervention. Detailed knowledge about the post-surgical state then lays the basis to assess the chances of a stable occlusion of the aneurysm required for a long-term treatment success. To this end, we propose mathematical and mechanical models of endovascular medical devices made out of thin metal wires. These can then be used for fully resolved flow simulations of the post-surgical blood flow, which in this work will be performed by means of a Lattice Boltzmann method applied to the incompressible Navier-Stokes equations and patient-specific geometries. To probe the suitability of homogenized models, we also investigate poro-elastic models to represent such medical devices. In particular, we examine the validity of this modeling approach for flow diverter placement across the opening of the aneurysm cavity. For both approaches, physiologically meaningful boundary conditions are provided from reduced-order models of the vascular system. The present study demonstrates our capabilities to predict the post-surgical state and lays a solid foundation to tackle the prediction of thrombus formation and, thus, the aneurysm occlusion in a next step.

math.NA↗

arXiv2024

A Comprehensive Numerical Approach to Coil Placement in Cerebral Aneurysms: Mathematical Modeling and In Silico Occlusion Classification

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well established and minimally invasive method, it bears the risk of sub-optimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted natural shape supporting the fixation within the aneurysm. For the spatial discretization our mathematical coil model is based on the Discrete Elastic Rod model which results in a dimension-reduced 1D system of differential equations. We include bending and twisting responses to account for the coils natural curvature. Collisions between coil segments and the aneurysm-wall are handled by an efficient contact algorithm that relies on an octree based collision detection. The numerical solution of the model is obtained by a symplectic semi-implicit Euler time stepping method. Our model can be easily incorporated into blood flow simulations of embolized aneurysms. In order to differentiate optimal from sub-optimal placements, we employ a suitable in silico Raymond-Roy type occlusion classification and measure the local packing density in the aneurysm at its neck, wall-region and core. We investigate the impact of uncertainties in the coil parameters and embolization procedure. To this end, we vary the position and the angle of insertion of the microcatheter, and approximate the local packing density distributions by evaluating sample statistics.

cs.CE↗