Accelerated simulation methodologies for computational vascular flow modelling.

Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier-Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.

Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study.

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first in silico observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time and shear rate, thus addressing the second question. We then address the third question by utilizing this calibrated model to provide new insight into the effects of hypertension on spontaneous thrombosis. We demonstrate how a mechanistic thrombosis model calibrated on an intracranial aneurysm cohort can help estimate spontaneous thrombosis prevalence in a broader aneurysm population. This study is enabled through a fully automatic multi-scale modeling pipeline. We use the clinical spontaneous thrombosis data as an indirect population-level validation of a complex computational modeling framework. Furthermore, our framework allows exploration of the influence of hypertension in spontaneous thrombosis. This lays the foundation for in silico clinical trials of cerebrovascular devices in high-risk populations, e.g., assessing the performance of flow diverters in aneurysms for hypertensive patients.

In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials.

The cost of clinical trials is ever-increasing. In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower these costs. We present the flow diverter performance assessment (FD-PASS) in-silico trial, which models the treatment of intracranial aneurysms in 164 virtual patients with 82 distinct anatomies with a flow-diverting stent, using computational fluid dynamics to quantify post-treatment flow reduction. The predicted FD-PASS flow-diversion success rates replicate the values previously reported in three clinical trials. The in-silico approach allows broader investigation of factors associated with insufficient flow reduction than feasible in a conventional trial. Our findings demonstrate that in-silico trials of endovascular medical devices can: (i) replicate findings of conventional clinical trials, and (ii) perform virtual experiments and sub-group analyses that are difficult or impossible in conventional trials to discover new insights on treatment failure, e.g. in the presence of side-branches or hypertension.

OpenMandible: An open-source framework for highly realistic numerical modelling of lower mandible physiology.

OBJECTIVE: Computer modeling of lower mandible physiology remains challenging because prescribing realistic material characteristics and boundary conditions from medical scans requires advanced equipment and skill sets. The objective of this study is to provide a framework that could reduce simplifications made and inconsistency (in terms of geometry, materials, and boundary conditions) among further studies on the topic. METHODS: The OpenMandible framework offers: 1) the first publicly available multiscale model of the mandible developed by combining cone beam computerized tomography (CBCT) and µCT imaging modalities, and 2) a C++ software tool for the generation of simulation-ready models (tet4 and hex8 elements). In addition to the application of conventional (Neumann and Dirichlet) boundary conditions, OpenMandible introduces a novel geodesic wave propagation - based approach for incorporating orthotropic micromechanical characteristics of cortical bone, and a unique algorithm for modeling muscles as uniformly directed vectors. The base intact model includes the mandible (spongy and compact bone), 14 teeth (comprising dentin, enamel, periodontal ligament, and pulp), simplified temporomandibular joints, and masticatory muscles (masseter, temporalis, medial, and lateral pterygoid). RESULTS: The complete source code, executables, showcases, and sample data are freely available on the public repository: https://github.com/ArsoVukicevic/OpenMandible. It has been demonstrated that by slightly editing the baseline model, one can study different "virtual" treatments or diseases, including tooth restoration, placement of implants, mandible bone degradation, and others. SIGNIFICANCE: OpenMandible eases the community to undertake a broad range of studies on the topic, while increasing their consistency and reproducibility. At the same time, the needs for dedicated equipment and skills for developing realistic simulation models are significantly reduced.

Population-specific modelling of between/within-subject flow variability in the carotid arteries of the elderly.

Computational fluid dynamics models are increasingly proposed for assisting the diagnosis and management of vascular diseases. Ideally, patient-specific flow measurements are used to impose flow boundary conditions. When patient-specific flow measurements are unavailable, mean values of flow measurements across small cohorts are used as normative values. In reality, both the between-subjects and within-subject flow variabilities are large. Consequently, neither one-shot flow measurements nor mean values across a cohort are truly indicative of the flow regime in a given person. We develop models for both the between-subjects and within-subject variability of internal carotid flow. A log-linear mixed effects model is combined with a Gaussian process to model the between-subjects flow variability, while a lumped parameter model of cerebral autoregulation is used to model the within-subject flow variability in response to heart rate and blood pressure changes. The model parameters are identified from carotid ultrasound measurements in a cohort of 103 elderly volunteers. We use the models to study intracranial aneurysm flow in 54 subjects under rest and exercise and conclude that OSI, a common wall shear-stress derived quantity in vascular CFD studies, may be too sensitive to flow fluctuations to be a reliable biomarker.

A computational model for prediction of clot platelet content in flow-diverted intracranial aneurysms.

Treatment of intracranial aneurysms with flow-diverting stents is a safe and minimally invasive technique. The goal is stable embolisation that facilitates stent endothelialisation, and elimination of the aneurysm. However, it is not fully understood why some aneurysms fail to develop a stable clot even with sufficient levels of flow reduction. Computational prediction of thrombus formation dynamics can help predict the post-operative response in such challenging cases. In this work, we propose a new model of thrombus formation and platelet dynamics inside intracranial aneurysms. Our novel contribution combines platelet activation and transport with fibrin generation, which is key to characterising stable and unstable thrombus. The model is based on two types of thrombus inside aneurysms: red thrombus (fibrin- and erythrocyte-rich) can be found in unstable clots, while white thrombus (fibrin- and platelet-rich) can be found in stable clots. The thrombus generation model is coupled to a CFD model and the flow-induced platelet index (FiPi) is defined as a quantitative measure of clot stability. Our model is validated against an in vitro phantom study of two flow-diverting stents with different sizing. We demonstrate that our model accurately predicts the lower thrombus stability in the oversized stent scenario. This opens possibilities for using computational simulations to improve endovascular treatment planning and reduce adverse events, such as delayed haemorrhage of flow-diverted aneurysms.

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease.

There is emerging evidence suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s-1 between the two cases.

Virtual endovascular treatment of intracranial aneurysms: models and uncertainty.

Virtual endovascular treatment models (VETMs) have been developed with the view to aid interventional neuroradiologists and neurosurgeons to pre-operatively analyze the comparative efficacy and safety of endovascular treatments for intracranial aneurysms. Based on the current state of VETMs in aneurysm rupture risk stratification and in patient-specific prediction of treatment outcomes, we argue there is a need to go beyond personalized biomechanical flow modeling assuming deterministic parameters and error-free measurements. The mechanobiological effects associated with blood clot formation are important factors in therapeutic decision making and models of post-treatment intra-aneurysmal biology and biochemistry should be linked to the purely hemodynamic models to improve the predictive power of current VETMs. The influence of model and parameter uncertainties associated to each component of a VETM is, where feasible, quantified via a random-effects meta-analysis of the literature. This allows estimating the pooled effect size of these uncertainties on aneurysmal wall shear stress. From such meta-analyses, two main sources of uncertainty emerge where research efforts have so far been limited: (1) vascular wall distensibility, and (2) intra/intersubject systemic flow variations. In the future, we suggest that current deterministic computational simulations need to be extended with strategies for uncertainty mitigation, uncertainty exploration, and sensitivity reduction techniques. WIREs Syst Biol Med 2017, 9:e1385. doi: 10.1002/wsbm.1385 For further resources related to this article, please visit the WIREs website.

Effects of Variations of Flow and Heart Rate on Intra-Aneurysmal Hemodynamics in a Ruptured Internal Carotid Artery Aneurysm During Exercise.

BACKGROUND: Hemodynamics is thought to play an important role in the mechanisms responsible for initiation, growth, and rupture of intracranial aneurysms. Computational fluid dynamic (CFD) analysis is used to assess intra-aneurysmal hemodynamics. OBJECTIVES: This study aimed to investigate the effects of variations in heart rate and internal carotid artery (ICA) flow rate on intra-aneurysmal hemodynamics, in an ICA aneurysm, by using computational fluid dynamics. PATIENTS AND METHODS: Computed tomography angiography (CTA) was performed in a 55 years old female case, with a saccular ICA aneurysm, to create a patient-specific geometrical anatomic model of the aneurysm. The intra-aneurysmal hemodynamic environments for three states with different flow and heart rates were analyzed using patient-specific image-based CFD modeling. RESULTS: Results showed significant changes for the three simulated states. For a proportion of the states examined, results were counterintuitive. Systolic and time-averaged wall shear stress and pressure on the aneurysm wall showed a proportional evolution with the mainstream flow rate. CONCLUSION: Results reinforced the pivotal role of vascular geometry, with respect to hemodynamics, together with the importance of performing patient-specific CFD analyses, through which the effect of different blood flow conditions on the aneurysm hemodynamics could be evaluated.

Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability.

Adverse wall shear stress (WSS) patterns are known to play a key role in the localisation, formation, and progression of intracranial aneurysms (IAs). Complex region-specific and time-varying aneurysmal WSS patterns depend both on vascular morphology as well as on variable systemic flow conditions. Computational fluid dynamics (CFD) has been proposed for characterising WSS patterns in IAs; however, CFD simulations often rely on deterministic boundary conditions that are not representative of the actual variations in blood flow. We develop a data-driven statistical model of internal carotid artery (ICA) flow, which is used to generate a virtual population of waveforms used as inlet boundary conditions in CFD simulations. This allows the statistics of the resulting aneurysmal WSS distributions to be computed. It is observed that ICA waveform variations have limited influence on the time-averaged WSS (TAWSS) on the IA surface. In contrast, in regions where the flow is locally highly multidirectional, WSS directionality and harmonic content are strongly affected by the ICA flow waveform. As a consequence, we argue that the effect of blood flow variability should be explicitly considered in CFD-based IA rupture assessment to prevent confounding the conclusions.

Modeling of the acute effects of primary hypertension and hypotension on the hemodynamics of intracranial aneurysms.

Hemodynamics is a risk factor in intracranial aneurysms (IA). Hypertension and pharmacologically induced hypotension are common in IA patients. This study investigates how hypertension and hypotension may influence aneurysmal hemodynamics. Images of 23 IAs at typical locations were used to build patient-specific Computational Fluid Dynamics models. The effects of hypotension and hypertension were simulated through boundary conditions by modulating the normotensive flow and pressure waveforms, in turn produced by a 1D systemic vascular model. Aneurysm location and flow pattern types were used to categorize the influence of hypotension and hypertension on relevant flow variables (velocity, pressure and wall shear stress). Results indicate that, compared to other locations, vertebrobasilar aneurysms (VBA) are more sensitive to flow changes. In VBAs, space-averaged velocity at peak systole increased by 30% in hypertension (16-21% in other locations). Flow in VBAs in hypotension decreased by 20% (10-13% in other locations). Momentum-driven hemodynamic types were also more affected by hypotension and hypertension, than shear-driven types. This study shows how patient-specific modeling can be effectively used to identify location-specific flow patterns in a clinically-relevant study, thus reinforcing the role played by modeling technologies in furthering our understanding of cardiovascular disease, and their potential in future healthcare.

Velocity Measurement in Carotid Artery: Quantitative Comparison of Time-Resolved 3D Phase-Contrast MRI and Image-based Computational Fluid Dynamics.

BACKGROUND: Understanding hemodynamic environment in vessels is important for realizing the mechanisms leading to vascular pathologies. OBJECTIVES: Three-dimensional velocity vector field in carotid bifurcation is visualized using TR 3D phase-contrast magnetic resonance imaging (TR 3D PC MRI) and computational fluid dynamics (CFD). This study aimed to present a qualitative and quantitative comparison of the velocity vector field obtained by each technique. SUBJECTS AND METHODS: MR imaging was performed on a 30-year old male normal subject. TR 3D PC MRI was performed on a 3 T scanner to measure velocity in carotid bifurcation. 3D anatomical model for CFD was created using images obtained from time-of-flight MR angiography. Velocity vector field in carotid bifurcation was predicted using CFD and PC MRI techniques. A statistical analysis was performed to assess the agreement between the two methods. RESULTS: Although the main flow patterns were the same for the both techniques, CFD showed a greater resolution in mapping the secondary and circulating flows. Overall root mean square (RMS) errors for all the corresponding data points in PC MRI and CFD were 14.27% in peak systole and 12.91% in end diastole relative to maximum velocity measured at each cardiac phase. Bland-Altman plots showed a very good agreement between the two techniques. However, this study was not aimed to validate any of methods, instead, the consistency was assessed to accentuate the similarities and differences between Time-resolved PC MRI and CFD. CONCLUSION: Both techniques provided quantitatively consistent results of in vivo velocity vector fields in right internal carotid artery (RCA). PC MRI represented a good estimation of main flow patterns inside the vasculature, which seems to be acceptable for clinical use. However, limitations of each technique should be considered while interpreting results.