A Rapid and Computationally Inexpensive Method to Virtually Implant Current and Next-Generation Stents into Subject-Specific Computational Fluid Dynamics Models

Computational modeling is often used to quantify hemodynamic alterations induced by stenting, but frequently uses simplified device or vascular representations. Based on a series of Boolean operations, we developed an efficient and robust method for assessing the influence of current and next-generation stents on local hemodynamics and vascular biomechanics quantified by computational fluid dynamics. Stent designs were parameterized to allow easy control over design features including the number, width and circumferential or longitudinal spacing of struts, as well as the implantation diameter and overall length. The approach allowed stents to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm constructed from medical imaging data. In the coronary bifurcation, we analyzed the hemodynamic difference between closed-cell and open-cell stent geometries. We investigated the impact of decreased strut size in stents with a constant porosity for increasing flow stasis within the stented basilar aneurysm model. These examples demonstrate the current method can be used to investigate differences in stent performance in complex vascular beds for a variety of stenting procedures and clinical scenarios

Which spring is the best? Comparison of methods for virtual stenting.

This paper presents a methodology for modeling the deployment of implantable devices used in minimally invasive vascular interventions. Motivated by the clinical need to perform preinterventional rehearsals of a stent deployment, we have developed methods enabling virtual device placement inside arteries, under the constraint of real-time application. This requirement of rapid execution narrowed down the search for a suitable method to the concept of a dynamic mesh. Inspired by the idea of a mesh of springs, we have found a novel way to apply it to stent modeling. The experiments conducted in this paper investigate properties of the stent models based on three different spring types: lineal, semitorsional, and torsional springs. Furthermore, this paper compares the results of various deployment scenarios for two different classes of devices: a stent graft and a flow diverter. The presented results can be of a high-potential clinical value, enabling the predictive evaluation of the outcome of a stent deployment treatment

Computational fluid dynamics study of bifurcation aneurysms treated with pipeline embolization device: side branch diameter study

An intracranial aneurysm, abnormal swelling of the cerebral artery, may lead to undesirable rates of mortality and morbidity upon rupture. Endovascular treatment involves the deployment of a flow-diverting stent that covers the aneurysm orifice, thereby reducing the blood flow into the aneurysm and mitigating the risk of rupture. In this study, computational fluid dynamics analysis is performed on a bifurcation model to investigate the change in hemodynamics with various side branch diameters. The condition after the deployment of a pipeline embolization device is also simulated. Hemodynamic factors such as flow velocity, pressure, and wall shear stress are studied. Aneurysms with a larger side branch vessel might have greater risk after treatment in terms of hemodynamics. Although a stent could lead to flow reduction entering the aneurysm, it would drastically alter the flow rate inside the side branch vessel. This may result in side-branch hypoperfusion subsequent to stenting. In addition, two patient-specific bifurcation aneurysms are tested, and the results show good agreement with the idealized models. Furthermore, the peripheral resistance of downstream vessels is investigated by varying the outlet pressure conditions. This quantitative analysis can assist in treatment planning and therapeutic decision-making.published_or_final_versio

The effects of stent porosity on the endovascular treatment of intracranial aneurysms located near a bifurcation

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Comparison of two stents in modifying cerebral aneurysm hemodynamics

There is a general lack of quantitative understanding about how specific design features of endovascular stents (struts and mesh design, porosity) affect the hemodynamics in intracranial aneurysms. To shed light on this issue, we studied two commercial high-porosity stents (Tristar stent™ and Wallstent®) in aneurysm models of varying vessel curvature as well as in a patientspecific model using Computational Fluid Dynamics. We investigated how these stents modify hemodynamic parameters such as aneurysmal inflow rate, stasis, and wall shear stress, and how such changes are related to the specific designs. We found that the flow damping effect of stents and resulting aneurysmal stasis and wall shear stress are strongly influenced by stent porosity, strut design, and mesh hole shape. We also confirmed that the damping effect is significantly reduced at higher vessel curvatures, which indicates limited usefulness of high-porosity stents as a stand-alone treatment. Finally, we showed that the stasis-inducing performance of stents in 3D geometries can be predicted from the hydraulic resistance of their flat mesh screens. From this, we propose a methodology to cost-effectively compare different stent designs before running a full 3D simulation

Improving Cardiovascular Stent Design Using Patient-Specific Models and Shape Optimization

Stent geometry influences local hemodynamic alterations (i.e. the forces moving blood through the cardiovascular system) associated with adverse clinical outcomes. Computational fluid dynamics (CFD) is frequently used to quantify stent-induced hemodynamic disturbances, but previous CFD studies have relied on simplified device or vascular representations. Additionally, efforts to minimize stent-induced hemodynamic disturbances using CFD models often only compare a small number of possible stent geometries. This thesis describes methods for modeling commercial stents in patient-specific vessels along with computational techniques for determining optimal stent geometries that address the limitations of previous studies. An efficient and robust method was developed for virtually implanting stent models into patient-specific vascular geometries derived from medical imaging data. Models of commercial stent designs were parameterized to allow easy control over design features. Stent models were then virtually implanted into vessel geometries using a series of Boolean operations. This approach allowed stented vessel models to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm to reveal how it can be used to investigate differences in hemodynamic performance in complex vascular beds for a variety of clinical scenarios. To identify hemodynamically optimal stents designs, a computational framework was constructed to couple CFD with a derivative-free optimization algorithm. The optimization algorithm was fully-automated such that solid model construction, mesh generation, CFD simulation and time-averaged wall shear stress (TAWSS) quantification did not require user intervention. The method was applied to determine the optimal number of circumferentially repeating stent cells (NC) for a slotted-tube stents and various commercial stents. Optimal stent designs were defined as those minimizing the area of low TAWSS. It was determined the optimal value of NC is dependent on the intrastrut angle with respect to the primary flow direction. Additionally, the geometries of current commercial stents were found to generally incorporate a greater NC than is hemodynamically optimal. The application of the virtual stent implantation and optimization methods may lead to stents with superior hemodynamic performance and the potential for improved clinical outcomes. Future in vivo studies are needed to validate the findings of the computational results obtained from the methods developed in this thesis

Machine learning and reduced order modelling for the simulation of braided stent deployment

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealised intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. The two-step workflow allows the classification of deployment conditions with up to 95% accuracy and real-time prediction of the stent deployed configuration with an average prediction error never greater than the spatial resolution of 3D rotational angiography (0.15 mm). These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration

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

Prognostic value of clinical neuroimaging in the investigation of minor ischaemic stroke and transient ischaemic attack

Stroke is the largest cause of adult neurological disability in the UK and up to 40% of disabling strokes are preceded by a minor ischaemic stroke or transient ischaemic attack (TIA). As the prompt initiation of preventative therapy can reduce the risk of recurrent stroke by up to 80%, there is a need for highly organised services and optimised secondary prevention therapies. Neuroimaging is fundamental to the investigation of cerebrovascular events and, when coupled with prognostic information, can contribute to tailored secondary prevention therapy. In this thesis, I aimed to provide new insights into the role of neuroimaging in the prognostication of minor ischaemic stroke and TIA, in order to assist clinical decision making and patient counselling. Data used in this thesis have been obtained from the Oxford Vascular Study (OXVASC); an ongoing prospective, population-based incidence study of vascular disease in Oxfordshire, operational since 1st April 2002. The OXVASC population comprises around 93,000 individuals, predominately Caucasian, defined by registration with one of nine primary care practices. Multiple overlapping methods are used to identify all patients with acute vascular events. Patients consecutively recruited to OXVASC with minor ischaemic stroke or TIA, irrespective of age, were included. All imaging was performed at the John Radcliffe Hospital and was with magnetic resonance imaging (MRI)/ MR-angiogram, or computed tomography (CT)/ CT-angiogram if MRI was contraindicated. I reviewed all imaging blinded to the report of the study neuroradiologist. I report several key findings in this thesis. First, intracranial atherosclerotic stenosis (ICS) was found in 19% of patients with the highest rates at older ages (21.2% at age ≥90 years). Although symptomatic ICS conveyed an increased risk of ischaemic stroke compared to no ICS (adjusted hazard ratio [HR]= 2.1, 95% CI 1.1- 3.7), the risks of same-territory ischaemic stroke in patients with 70- 99% symptomatic ICS tended to be less than those reported in the non-stenting arms of the trials, validating the role of intensive medical management in routine clinical practice. Asymptomatic ICS did not convey additional risk of vascular events or death. Second, diffusion-weighted imaging (DWI) lesions predicted an increased risk of recurrent ischaemic stroke after minor ischaemic stroke with NIHSS 0-1 and TIA up to 10 years (HR= 3.0, 1.3- 7.1 and 2.7, 1.3- 5.5, respectively), and the strongest predictive value was in patients with a cryptogenic aetiology (HR= 4.7, 1.7- 12.9). Third, 5% of patients referred to an acute neurovascular clinic harbour an asymptomatic, incidental unruptured intracranial aneurysm. Although this is almost double the rate in the general population, the subsequent risk of aneurysm rupture was low (4.6 subarachnoid haemorrhages per 1,000 person-years) in the context of intensively managed vascular risk factors and guideline-based surveillance and intervention