Mona Alimohammadi

Development of a patient-specific simulation tool to analyse aortic dissections: Assessment of mixed patient-specific flow and pressure boundary conditions

Aortic dissection has high morbidity and mortality rates and guidelines regarding surgical intervention are not clearly defined. The treatment of aortic dissection varies with each patient and detailed knowledge of haemodynamic and mechanical forces would be advantageous in the process of choosing a course of treatment. In this study, a patient-specific dissected aorta geometry is constructed from computed tomography scans. Dynamic boundary conditions are implemented by coupling a three element Windkessel model to the 3D domain at each outlet, in order to capture the essential behaviour of the downstream vasculature. The Windkessel model parameters are defined based on clinical data. The predicted minimum and maximum pressures are close to those measured invasively. Malperfusion is indicated and complex flow patterns are observed. Pressure, flow and wall shear stress distributions are analysed. The methodology presented here provides insight into the haemodynamics in a patient-specific dissected aorta and represents a development towards the use of CFD simulations as a diagnostic tool for aortic dissection.

Aortic dissection simulation models for clinical support: fluid-structure interaction vs. rigid wall models

BackgroundThe management and prognosis of aortic dissection (AD) is often challenging and the use of personalised computational models is being explored as a tool to improve clinical outcome. Including vessel wall motion in such simulations can provide more realistic and potentially accurate results, but requires significant additional computational resources, as well as expertise. With clinical translation as the final aim, trade-offs between complexity, speed and accuracy are inevitable. The present study explores whether modelling wall motion is worth the additional expense in the case of AD, by carrying out fluid-structure interaction (FSI) simulations based on a sample patient case.MethodsPatient-specific anatomical details were extracted from computed tomography images to provide the fluid domain, from which the vessel wall was extrapolated. Two-way fluid-structure interaction simulations were performed, with coupled Windkessel boundary conditions and hyperelastic wall properties. The blood was modelled using the Carreau-Yasuda viscosity model and turbulence was accounted for via a shear stress transport model. A simulation without wall motion (rigid wall) was carried out for comparison purposes.ResultsThe displacement of the vessel wall was comparable to reports from imaging studies in terms of intimal flap motion and contraction of the true lumen. Analysis of the haemodynamics around the proximal and distal false lumen in the FSI model showed complex flow structures caused by the expansion and contraction of the vessel wall. These flow patterns led to significantly different predictions of wall shear stress, particularly its oscillatory component, which were not captured by the rigid wall model.ConclusionsThrough comparison with imaging data, the results of the present study indicate that the fluid-structure interaction methodology employed herein is appropriate for simulations of aortic dissection. Regions of high wall shear stress were not significantly altered by the wall motion, however, certain collocated regions of low and oscillatory wall shear stress which may be critical for disease progression were only identified in the FSI simulation. We conclude that, if patient-tailored simulations of aortic dissection are to be used as an interventional planning tool, then the additional complexity, expertise and computational expense required to model wall motion is indeed justified.

Evaluation of the hemodynamic effectiveness of aortic dissection treatments via virtual stenting.

Aortic dissection treatment varies for each patient and stenting is one of a number of approaches that are utilized to Stabilize the condition. Information regarding the hemodynamic forces in the aorta in dissected and virtually stented cases could support clinicians in their choices of treatment prior to medical intervention. Computational fluid dynamics coupled with lumped parameter models have shown promise in providing detailed information that could be used in the clinic; for this, it is necessary to develop personalized workflows in order to produce patient-specific simulations. In the present study, a case of pre- and post-stenting (virtual stent-graft) of an aortic dissection is investigated with a particular focus on the role of personalized boundary conditions. For each virtual case, velocity, pressure, energy loss, and wall shear stress values are evaluated and compared. The simulated single stent-graft only marginally reduced the pulse pressure and systemic energy loss. The double stent-graft results showed a larger reduction in pulse pressure and a 40% reduction in energy loss as well as a more physiological wall shear stress distribution. Regions of potential risk were highlighted. The methodology applied in the present study revealed detailed information about two possible surgical outcome cases and shows promise as both a diagnostic and an interventional tool.

Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection

Vascular calcification results in stiffening of the aorta and is associated with hypertension and atherosclerosis. Atherogenesis is a complex, multifactorial, and systemic process; the result of a number of factors, each operating simultaneously at several spatial and temporal scales. The ability to predict sites of atherogenesis would be of great use to clinicians in order to improve diagnostic and treatment planning. In this paper, we present a mathematical model as a tool to understand why atherosclerotic plaque and calcifications occur in specific locations. This model is then used to analyze vascular calcification and atherosclerotic areas in an aortic dissection patient using a mechanistic, multi-scale modeling approach, coupling patient-specific, fluid-structure interaction simulations with a model of endothelial mechanotransduction. A number of hemodynamic factors based on state-of-the-art literature are used as inputs to the endothelial permeability model, in order to investigate plaque and calcification distributions, which are compared with clinical imaging data. A significantly improved correlation between elevated hydraulic conductivity or volume flux and the presence of calcification and plaques was achieved by using a shear index comprising both mean and oscillatory shear components (HOLMES) and a non-Newtonian viscosity model as inputs, as compared to widely used hemodynamic indicators. The proposed approach shows promise as a predictive tool. The improvements obtained using the combined biomechanical/biochemical modeling approach highlight the benefits of mechanistic modeling as a powerful tool to understand complex phenomena and provides insight into the relative importance of key hemodynamic parameters.

A multiscale modelling approach to understand atherosclerosis formation: A patient-specific case study in the aortic bifurcation

Atherogenesis, the formation of plaques in the wall of blood vessels, starts as a result of lipid accumulation (low-density lipoprotein cholesterol) in the vessel wall. Such accumulation is related to the site of endothelial mechanotransduction, the endothelial response to mechanical stimuli and haemodynamics, which determines biochemical processes regulating the vessel wall permeability. This interaction between biomechanical and biochemical phenomena is complex, spanning different biological scales and is patient-specific, requiring tools able to capture such mathematical and biological complexity in a unified framework. Mathematical models offer an elegant and efficient way of doing this, by taking into account multifactorial and multiscale processes and mechanisms, in order to capture the fundamentals of plaque formation in individual patients. In this study, a mathematical model to understand plaque and calcification locations is presented: this model provides a strong interpretability and physical meaning through a multiscale, complex index or metric (the penetration site of low-density lipoprotein cholesterol, expressed as volumetric flux). Computed tomography scans of the aortic bifurcation and iliac arteries are analysed and compared with the results of the multifactorial model. The results indicate that the model shows potential to predict the majority of the plaque locations, also not predicting regions where plaques are absent. The promising results from this case study provide a proof of concept that can be applied to a larger patient population.

Computational Methods for Patient-Specific Modelling

Patient-specific haemodynamic modelling using computational fluid dynamics approaches is a multi-stage process. Firstly, a model of the geometry of interest is created and discretised. The governing equations for the fluid must then be solved for each discretised element, and the interfaces of the domain should be treated appropriately. This chapter will describe the basic equations solved using CFD and their numerical treatment. Subsequently, the stages required in imaging the patient and converting the images into a 3D geometry will be given, followed by a brief description of discretisation (meshing). The mathematics behind lumped-parameter modelling to develop dynamic BCs will be described and a comparison with alternative BCs will be made. Finally, a brief introduction to the relevant aspects of solid modelling will be provided.

Role of Vessel Wall Motion in Aortic Dissection

In this chapter, two-way fluid-structure interaction (FSI) simulations are performed and results are compared to rigid wall simulations for the pre-operative case analysed in the previous chapter, in order to evaluate the importance of considering vessel wall and intimal flap motion.

Effectiveness of Aortic Dissection Treatments via Virtual Stenting

In this chapter, pre-operative and virtual-stenting scenarios are applied to the same patient model that was discussed in the previous chapter.

Haemodynamics of a Dissected Aorta

In this chapter, a three-element Windkessel model is coupled to each of the outflow boundaries of a 3D geometry from a patient with a type-B AD.