Patient-specific modelling of the cerebral circulation for aneurysm risk assessment

Cerebral aneurysms are localised pathological dilatations of cerebral arteries, most commonly found in the circle of Willis. Although not all aneurysms are unstable, the major clinical concern involved is the risk of rupture. High morbidity and mortality rates are associated with the haemorrhage resulting from rupture. New indicators of aneurysm stability are sought, since current indicators based on morphological factors have been shown to be unreliable. Haemodynamical factors are known to be relevant in vascular wall remodelling, and therefore believed to play an important role in aneurysmdevelopment and stability. Studies suggest that intra-aneurysmal wall shear stress and flow patterns, for example, are candidate parameters in aneurysm stability assessment. These factors can be estimated if the 3D patient-specific intra-aneurysmal velocity is known, which can be obtained via a combination of in vivo measurements and computational fluid dynamics models. The main determinants of the velocity field are the vascular geometry and flow through this geometry. Over the last decade the extraction of the vascular geometry has become well established. More recently, there has been a shift of attention towards extracting boundary conditions for the 3D vascular segment of interest. The aim of this research is to improve the reliability of the model-based representation of the velocity field in the aneurysmal sac. To this end, a protocol is proposed such that patient-specific boundary conditions for the 3D segment of interest can be estimated without the need for added invasive procedures. This is facilitated by a 1D wave propagation model based on patient-specific geometry and boundary conditions measured non-invasively in more accessible regions. Such a protocol offers improved statistical reliability owing to the increased number of patients that can participate in studies aiming to identify parameters of interest in aneurysm stability assessment. In chapter 2 the intra-aneurysmal velocity field in an idealised aneurysm model is validated with particle image velocimetry experiments, after which the flow patterns are evaluated using a vortex identification method. Chapter 3 describes a 1D model wave propagation model of the cerebral circulation with a patient-specific vascular geometry. The resulting flow pulses at the boundaries of the 3D segment of interest are compared to those obtained with a patient-generic geometry. The influence of these different boundary conditions on the 3D intra-aneurysmal velocity field is evaluated in chapter 4 by prescribing the end-diastolic flows extracted from the 1D models. In order to measure blood flow with videodensitometric methods, an injection of contrast agent is required. The effect of this injection on the flow of interest is assessed in chapter 5. In chapter 6, pressure measurements in the internal carotid are used to evaluate the variability of pressure waveform and its effect on the boundary conditions for the 1D model. Finally, a protocol for full patient-specific modelling is discussed in chapter 7

Multi-time-lag PIV analysis of steady and pulsatile flows in a sidewall aneurysm

The effect of inflow waveform on the hemodynamics of a real-size idealized sidewall intracranial aneurysm (IA) model was investigated using particle imaging velocimetry (PIV). For this purpose, we implemented an error analysis based on several PIV measurements with different time lags to ensure high precision of velocity fields measured in both the IA and the parent artery. The relative error measured in the main part of the circulating volume was <1% despite the three orders of magnitude difference of parent artery and IA dome velocities. Moreover, important features involved in IA evolution were potentially emphasized from the qualitative and quantitative flow pattern comparison resulting from steady and unsteady inflows. In particular, the flow transfer in IA and the vortical structure were significantly modified when increasing the number of harmonics for a typical physiological flow, in comparison with quasi-steady conditions

In-silico clinical trials for assessment of intracranial flow diverters

In-silico trials refer to pre-clinical trials performed, entirely or in part, using individualised computer models that simulate some aspect of drug effect, medical device, or clinical intervention. Such virtual trials reduce and optimise animal and clinical trials, and enable exploring a wider range of anatomies and physiologies. In the context of endovascular treatment of intracranial aneurysms, in-silico trials can be used to evaluate the effectiveness of endovascular devices over virtual populations of patients with different aneurysm morphologies and physiologies. However, this requires (i) a virtual endovascular treatment model to evaluate device performance based on a reliable performance indicator, (ii) models that represent intra- and inter-subject variations of a virtual population, and (iii) creation of cost-effective and fully-automatic workflows to enable a large number of simulations at a reasonable computational cost and time. Flow-diverting stents have been proven safe and effective in the treatment of large wide-necked intracranial aneurysms. The presented thesis aims to provide the ingredient models of a workflow for in-silico trials of flow-diverting stents and to enhance the general knowledge of how the ingredient models can be streamlined and accelerated to allow large-scale trials. This work contributed to the following aspects: 1) To understand the key ingredient models of a virtual treatment workflow for evaluation of the flow-diverter performance. 2) To understand the effect of input uncertainty and variability on the workflow outputs, 3) To develop generative statistical models that describe variability in internal carotid artery flow waveforms, and investigate the effect of uncertainties on quantification of aneurysmal wall shear stress, 4) As part of a metric to evaluate success of flow diversion, to develop and validate a thrombosis model to assess FD-induced clot stability, and 5) To understand how a fully-automatic aneurysm flow modelling workflow can be built and how computationally inexpensive models can reduce the computational costs

Multiscale Fluid-Structure Interaction Models Development and Applications to the 3D Elements of a Human Cardiovascular System

Cardiovascular diseases (CVD) are the number one cause of death of humans in the United States and worldwide. Accurate, non-invasive, and cheaper diagnosis methods have always been on demand as cardiovascular monitoring increase in prevalence. The primary causes of the various forms of these CVDs are atherosclerosis and aneurysms in the blood vessels. Current noninvasive methods (i.e., statistical/medical) permit fairly accurate detection of the disease once clinical symptoms are suggestive of the existence of hemodynamic disorders. Therefore, the recent surge of hemodynamics models facilitated the prediction of cardiovascular conditions. The hemodynamic modeling of a human circulatory system involves varying levels of complexity which must be accounted for and resolved. Pulse-wave propagation effects and high aspect-ratio segments of the vasculature are represented using a quasi-one-dimensional (1D), non-steady, averaged over the cross-section models. However, these reduced 1D models do not account for the blood flow patterns (recirculation zones), vessel wall shear stresses and quantification of repetitive mechanical stresses which helps to predict a vessel life. Even a whole three-dimensional (3D) modeling of the vasculature is computationally intensive and do not fit the timeline of practical use. Thus the intertwining of a quasi 1D global vasculature model with a specific/risk-prone 3D local vessel ones is imperative. This research forms part of a multiphysics project that aims to improve the detailed understanding of the hemodynamics by investigating a computational model of fluid-structure interaction (FSI) of in vivo blood flow. First idealized computational a 3D FSI artery model is configured and executed in ANSYS Workbench, forming an implicit coupling of the blood flow and vessel walls. Then the thesis focuses on an approach developed to employ commercial tools rather than in-house mathematical models in achieving multiscale simulations. A robust algorithm is constructed to combine stabilization techniques to simultaneously overcome the added-mass effect in 3D FSI simulation and mathematical difficulties such as the assignment of boundary conditions at the interface between the 3D-1D coupling. Applications can be of numerical examples evaluating the change of hemodynamic parameters and diagnosis of an abdominal aneurysm, deep vein thrombosis, and bifurcation areas

Comparison of existing aneurysm models and their path forward

The two most important aneurysm types are cerebral aneurysms (CA) and abdominal aortic aneurysms (AAA), accounting together for over 80\% of all fatal aneurysm incidences. To minimise aneurysm related deaths, clinicians require various tools to accurately estimate its rupture risk. For both aneurysm types, the current state-of-the-art tools to evaluate rupture risk are identified and evaluated in terms of clinical applicability. We perform a comprehensive literature review, using the Web of Science database. Identified records (3127) are clustered by modelling approach and aneurysm location in a meta-analysis to quantify scientific relevance and to extract modelling patterns and further assessed according to PRISMA guidelines (179 full text screens). Beside general differences and similarities of CA and AAA, we identify and systematically evaluate four major modelling approaches on aneurysm rupture risk: finite element analysis and computational fluid dynamics as deterministic approaches and machine learning and assessment-tools and dimensionless parameters as stochastic approaches. The latter score highest in the evaluation for their potential as clinical applications for rupture prediction, due to readiness level and user friendliness. Deterministic approaches are less likely to be applied in a clinical environment because of their high model complexity. Because deterministic approaches consider underlying mechanism for aneurysm rupture, they have improved capability to account for unusual patient-specific characteristics, compared to stochastic approaches. We show that an increased interdisciplinary exchange between specialists can boost comprehension of this disease to design tools for a clinical environment. By combining deterministic and stochastic models, advantages of both approaches can improve accessibility for clinicians and prediction quality for rupture risk.Comment: 46 pages, 5 figure

Clinical application of image‐based CFD for cerebral aneurysms

During the last decade, the convergence of medical imaging and computational modeling technologies has enabled tremendous progress in the development and application of image‐based computational fluid dynamics modeling of patient‐specific blood flows. These techniques have been used for studying the basic mechanisms involved in the initiation and progression of vascular diseases, for studying possible ways to improve the diagnosis and evaluation of patients by incorporating hemodynamics information to the anatomical data typically available, and for the development of computational tools that can be used to improve surgical and endovascular treatment planning. However, before these technologies can have a significant impact on the routine clinical practice, it is still necessary to demonstrate the connection between the extra information provided by the models and the natural progression of vascular diseases and the outcome of interventions. This paper summarizes some of our contributions in this direction, focusing in particular on cerebral aneurysms