Multivariate analysis of hemodynamic parameters on intracranial aneurysm initiation of the internal carotid artery

Hemodynamic analysis of thrombosed intracranial aneurysms: a comparative correlation study

A small fraction of intracranial aneurysms (IA) contains intrasaccular thrombosis (IST). This study explores the hemodynamic causes of IST formation in IAs. We performed computational hemodynamic analysis in 26 IAs: 13 thrombosed and 13 non-thrombosed. The IAs in the two cohorts were matching in size and anatomical location. The computational hemodynamic analysis used "patient-specific" IA geometries derived from 3D magnetic resonance imaging. A comprehensive hemodynamic analysis was conducted using commonly used hemodynamic metrics (e.g., wall shear stress [WSS] and its derivative), flow vortex analysis, and velocity informatics. We observed that more flow eddies and endothelial cell activation potential (ECAP) are present in thrombosed IAs. The flow eddies in thrombosed IAs change size and location over a cardiac cycle more significantly than those in non-thrombosed IAs. These two factors, coupled with more flow stagnation and positive WSS divergence in the thrombosed IAs, promoted thrombus formation in the thrombosed IA cohort.

Investigating the role of blood models in predicting rupture status of intracranial aneurysms

Purpose. Selecting patients with high-risk intracranial aneurysms (IAs) is of clinical importance. Recent work in machine learning-based (ML) predictive modeling has demonstrated that lesion-specific hemodynamics within IAs can be combined with other information to provide critical insights for assessing rupture risk. However, how the adoption of blood rheology models (i.e., Newtonian and Non-Newtonian blood models) may influence ML-based predictive modeling of IA rupture risk has not been investigated.Methods and Materials.In this study, we conducted transient CFD simulations using Newtonian and non-Newtonian rheology (Carreau-Yasuda [CY]) models on a large cohort of 'patient-specific' IA geometries (>100) under pulsatile flow conditions to investigate how each blood model may affect the characterization of the IAs' rupture status. Key hemodynamic parameters were analyzed and compared, including wall shear stress (WSS) and vortex-based parameters. In addition, velocity-informatics features extracted from the flow velocity were utilized to train a support vector machine (SVM) model for rupture status prediction.Results.Our findings demonstrate significant differences between the two models (i.e., Newtonian versus CY) regarding the WSS-related metrics. In contrast, the parameters derived from the flow vortices and velocity informatics agree. Similar to other studies, using a non-Newtonian CY model results in lower peak WSS and higher oscillatory shear index (OSI) values. Furthermore, integrating velocity informatics and machine learning achieved robust performance for both blood models (area under the curve [AUC] ˃0.85).Conclusions.Our preliminary study found that ML-based rupture status prediction derived from velocity informatics and geometrical parameters yielded comparable results despite differences observed in aneurysmal hemodynamics using two blood rheology models (i.e., Newtonian versus CY).

Improving Prediction of Intracranial Aneurysm Rupture Status Using Temporal Velocity-Informatics

This study uses a spatial pattern analysis of time-resolved aneurysmal velocity fields to enhance the characterization of intracranial aneurysms' (IA) rupture status. We name this technique temporal velocity-informatics (TVI). In this study, using imaging data obtained from 112 subjects harboring IAs with known rupture status, we reconstructed 3D models to get aneurysmal velocity data by performing computational fluid dynamics (CFD) simulations and morphological information. TVI analyses were conducted for time-resolved velocity fields to quantitatively obtain spatial and temporal flow disturbance. Lastly, we employed four machine learning (ML) methods (e.g., support vector machine [SVM]) to evaluate the prediction performance of the proposed TVI. Overall, the SVM's prediction with TVI performed the best: an area under the curve (AUC) value of 0.92 and a total accuracy of 86%. With TVI, the SVM classifier correctly identified 77 and 92% of ruptured and unruptured IAs, respectively.

Amplification of Secondary Flow at the Initiation Site of Intracranial Sidewall Aneurysms

PURPOSE

The initiation of intracranial aneurysms has long been studied, mainly by the evaluation of the wall shear stress field. However, the debate about the emergence of hemodynamic stimuli still persists. This paper builds on our previous hypothesis that secondary flows play an important role in the formation cascade by examining the relationship between flow physics and vessel geometry.

METHODS

A composite evaluation framework was developed to analyze the simulated flow field in perpendicular cross-sections along the arterial centerline. The velocity field was decomposed into secondary flow components around the centerline in these cross-sections, allowing the direct comparison of the flow features with the geometrical parameters of the centerline. Qualitative and statistical analysis was performed to identify links between morphology, flow, and the formation site of the aneurysms.

RESULTS

The normalized mean curvature and curvature peak were significantly higher in the aneurysmal bends than in other arterial bends. Similarly, a significant difference was found for the normalized mean velocity ( p = 0.0274 ), the circumferential ( p = 0.0029 ), and radial ( p = 0.0057 ) velocity components between the arterial bends harboring the aneurysm than in other arterial bends. In contrast, the difference of means for the normalized axial velocity is insignificant ( p = 0.1471 ).

CONCLUSION

Thirty cases with aneurysms located on the ICA were analyzed in the virtually reconstructed pre-aneurysmal state by an in-silico study. We found that sidewall aneurysm formation on the ICA is more probable in these arterial bends with the highest case-specific curvature, which are accompanied by the highest case-specific secondary flows (circumferential and radial velocity components) than in other bends.

Improving rupture status prediction for intracranial aneurysms using wall shear stress informatics

BACKGROUND

Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.e., ruptured versus unruptured).

METHODS

"Patient-specific" computational fluid dynamics (CFD) simulations were performed for 112 IAs; each IA's rupture status was known from medical records. Recall that CFD-simulated hemodynamics data (wall shear stress and its derivatives) are located on unstructured meshes. Hence, we mapped WSS data from an unstructured grid onto a unit disk (i.e., a uniformly sampled polar coordinate system); data in a uniformly sampled polar system is equivalent to image data. Mapped WSS data (onto the unit disk) were readily available for Radiomics analysis to extract spatial patterns of WSS data. We named this innovative technology "WSS-informatics" (i.e., using informatics techniques to analyze WSS data); the usefulness of WSS-informatics was demonstrated during the predictive modeling of IAs' rupture status.

RESULTS

None of the conventional WSS parameters correlated to IAs' rupture status. However, WSS-informatics metrics were discriminative (p-value < 0.05) to IAs' rupture status. Furthermore, predictive models with WSS-informatics features could significantly improve the prediction performance (area under the receiver operating characteristic curve [AUROC]: 0.78 vs. 0.85; p-value < 0.01).

CONCLUSION

The proposed innovations enabled the first study to use spatial patterns of WSS data to improve the predictive modeling of IAs' rupture status.

Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study

Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85-0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.

Intracranial aneurysms in sickle cell disease are associated with hemodynamic stress and anemia

ABSTRACT

Although hemodynamic stress plays a key role in aneurysm formation outside of sickle cell disease (SCD), its role is understudied in patients with SCD. We hypothesized that tissue-based markers of hemodynamic stress are associated with aneurysm presence in a prospective SCD cohort. Children and adults with SCD, with and without aneurysms, underwent longitudinal brain magnetic resonance imaging/magnetic resonance angiography (MRA) to assess cerebral blood flow (CBF) and oxygen extraction fraction (OEF). Baseline characteristics were recorded. In the subgroup of adults, stepwise mixed-effect logistic regression examined clinical variables, CBF, and OEF as predictors of aneurysm presence. Cumulative rates of new aneurysm formation were estimated using Kaplan-Meier analyses. Forty-three aneurysms were found in 27 of 155 patients (17%). Most aneurysms were ≤3 mm and in the intracranial internal carotid artery. On univariate analysis, older age (P = .07), lower hemoglobin (P = .002), higher CBF (P = .03), and higher OEF (P = .02) were associated with aneurysm presence. On multivariable analysis, age and CBF remained independently associated with aneurysm presence. Seventy-six patients (49% of enrollment) received follow-up MRAs (median, 3.5 years). No aneurysm grew or ruptured, however, 7 new aneurysms developed in 6 patients. The 3-year cumulative rate of aneurysm formation was 3.5%. In 155 patients with SCD, 17% had intracranial aneurysms. Three-year aneurysm formation rate was 3.5%, although limited by small longitudinal sample size and short follow-up duration. Aneurysm presence was associated with elevated CBF in adults, as a tissue-based marker of cerebral hemodynamic stress. Future studies may examine the predictive role of CBF in aneurysm development in SCD.

Exploring a frequency-domain attention-guided cascade U-Net: Towards spatially tunable segmentation of vasculature

Developing fully automatic and highly accurate medical image segmentation methods is critically important for vascular disease diagnosis and treatment planning. Although advances in convolutional neural networks (CNNs) have spawned an array of automatic segmentation models converging to saturated high performance, none have explored whether CNNs can achieve (spatially) tunable segmentation. As a result, we propose multiple attention modules from a frequency-domain perspective to construct a unified CNN architecture for segmenting vasculature with desired (spatial) scales. The proposed CNN architecture is named frequency-domain attention-guided cascaded U-Net (FACU-Net). Specifically, FACU-Net contains two innovative components: (1) a frequency-domain-based channel attention module that adaptively tunes channel-wise feature responses and (2) a frequency-domain-based spatial attention module that enables the deep network to concentrate on foreground regions of interest (ROIs) effectively. Furthermore, we devised a novel frequency-domain-based content attention module to enhance or weaken the high (spatial) frequency information, allowing us to strengthen or eliminate vessels of interest. Extensive experiments using clinical data from patients with intracranial aneurysms (IA) and abdominal aortic aneurysms (AAA) demonstrated that the proposed FACU-Net met its design goal. In addition, we further investigated the association between varying (spatial) frequency components and the desirable vessel size/scale attributes. In summary, our preliminary findings are encouraging, and further developments may lead to deployable image segmentation models that are spatially tunable for clinical applications.

Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience

Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively.

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation. Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models. The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ∼2 h to ∼10 min). Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.

USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION

"Image-based" computational fluid dynamics (CFD) simulations provide insights into each patient's hemodynamic environment. However, current standard procedures for creating CFD models start with manual segmentation and are time-consuming, hindering the clinical translation of image-based CFD simulations. This feasibility study adopts deep-learning-based image segmentation (hereafter referred to as Artificial Intelligence (AI) segmentation) to replace manual segmentation to accelerate CFD model creation. Two published convolutional neural network-based AI methods (MIScnn and DeepMedic) were selected to perform CFD model extraction from three-dimensional (3D) rotational angiography data containing intracranial aneurysms. In this study, aneurysm morphological and hemodynamic results using models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICC), Bland-Altman plots, and Pearson's correlation coefficients (PCC) were combined to assess how well AI-generated CFD models were performed. We found that almost perfect agreement was obtained between the human and AI results for all eleven morphological and five out of eight hemodynamic parameters, while a moderate agreement was obtained from the remaining three hemodynamic parameters. Given this level of agreement, using AI segmentation to create CFD models is feasible, given more developments.

Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms?

Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP) algorithms to explain and analyze 6 Ml algorithms. All models performed comparably: LR area under the curve (AUC) was 0.71; SVM AUC was 0.76; RF AUC was 0.73; XGBoost AUC was 0.78; MLPNN AUC was 0.73; BART AUC was 0.73. Our interpretability analysis demonstrated consistent results across all the methods; i.e., the utility of the top 12 features was broadly consistent. Furthermore, contributions of 9 important features (aneurysm area, aneurysm location, aneurysm type, wall shear stress maximum during systole, ostium area, the size ratio between aneurysm width, (parent) vessel diameter, one standard deviation among time-averaged low shear area, and one standard deviation of temporally averaged low shear area less than 0.4 Pa) were nearly the same. This research suggested that ML classifiers can provide explainable predictions consistent with general domain knowledge concerning IA rupture. With the improved understanding of ML algorithms, clinicians' trust in ML algorithms will be enhanced, accelerating their clinical translation.

Impact of spatial and temporal stability of flow vortices on vascular endothelial cells

PURPOSE

Intracranial aneurysms (IAs) are pathological dilations of cerebrovascular vessels due to degeneration of the mechanical strength of the arterial wall, precluded by altered cellular functionality. The presence of swirling hemodynamic flow (vortices) is known to alter vascular endothelial cell (EC) morphology and protein expression indicative of IAs. Unfortunately, less is known if vortices with varied spatial and temporal stability lead to differing levels of EC change. The aim of this work is to investigate vortices of varying spatial and temporal stability impact on ECs.

METHODS

Vortex and EC interplay was investigated by a novel combination of parallel plate flow chamber (PPFC) design and computational analysis. ECs were exposed to laminar (7.5 dynes/[Formula: see text] wall shear stress) or low (<1 dynes/[Formula: see text]) stress vortical flow using PPFCs. Immunofluorescent imaging analyzed EC morphology, while ELISA tests quantified VE-cadherin (cell-cell adhesion), VCAM-1 (macrophage-EC adhesion), and cleaved caspase-3 (apoptotic signal) expression. PPFC flow was simulated, and vortex stability was calculated via the temporally averaged degree of (volume) overlap (TA-DVO) of vortices within a given area.

RESULTS

EC morphological changes were independent of vortex stability. Increased stability promoted VE-cadherin degradation (correlation coefficient r = [Formula: see text]0.84) and 5-fold increased cleaved caspase-3 post 24 h in stable (TA-DVO 0.736 ± 0.05) vs unstable (TA-DVO 0.606 [Formula: see text]0.2) vortices. ECs in stable vortices displayed a 4.5-fold VCAM-1 increase than unstable counterparts after 12 h.

CONCLUSION

This work demonstrates highly stable disturbed flow imparts increased inflammatory signaling, degraded cell-cell adhesion, and increased cellular apoptosis than unstable vortices. Such knowledge offers novel insight toward understanding IA development and rupture.

Hemodynamic indicators of the formation of tandem intracranial aneurysm based on a vascular restoration algorithm

Background

Hemodynamic factors are believed to be closely related to IA growth. However, the underlying pathophysiological mechanism that induces the growth sequence in tandem intracranial aneurysms (IAs) remains unclear.

Methods and results

This study involved five patients with tandem IAs. Aneurysm models were reconstructed based on image datasets. A novel vascular restoration algorithm was proposed to generate the hypothetical geometry of the healthy parent vessel before each IA formation in the concatenated structure. Detailed hemodynamic patterns and morphological features were revealed under various growth sequences of tandem IAs to investigate the flow-driven mechanism of IA growth. Potential hemodynamic indicators of IA formation were proposed.

Results

The patient cases were divided into two groups based on the size difference of tandem IAs. In the group with a similar size of tandem IAs, the position of the vortex core was associated with the site of the secondary aneurysm, while in the group with a significant size difference of the IAs, the position with the maximum curvature of the parent vessel plays a significant role in aneurysm formation.

Conclusions

This study preliminarily revealed key hemodynamic and morphological indicators that determine the formation of tandem IAs. The proposed vascular restoration algorithm that provided the pre-aneurysm vasculature might be useful in investigating the flow-driven mechanism of IA growth, thus contributing to the risk evaluation of secondary aneurysm formation.

Computational fluid dynamic analysis of the initiation of cerebral aneurysms

OBJECTIVE

Relationships between aneurysm initiation and hemodynamic factors remain unclear since de novo aneurysms are rarely observed. Most previous computational fluid dynamics (CFD) studies have used artificially reproduced vessel geometries before aneurysm initiation for analysis. In this study, the authors investigated the hemodynamic factors related to aneurysm initiation by using angiographic images in patients with cerebral aneurysms taken before and after an aneurysm formation.

METHODS

The authors identified 10 cases of de novo aneurysms in patients who underwent follow-up examinations for existing cerebral aneurysms located at a different vessel. The authors then reconstructed the vessel geometry from the images that were taken before aneurysm initiation. In addition, 34 arterial locations without aneurysms were selected as control cases. Hemodynamic parameters acting on the arterial walls were calculated by CFD analysis.

RESULTS

In all de novo cases, the aneurysmal initiation area corresponded to the highest wall shear stress divergence (WSSD point), which indicated that there was a strong tensile force on the arterial wall at the initiation area. The other previously reported parameters did not show such correlations. Additionally, the pressure loss coefficient (PLc) was statistically significantly higher in the de novo cases (p < 0.01). The blood flow impact on the bifurcation apex, or the secondary flow accompanied by vortices, resulted in high tensile forces and high total pressure loss acting on the vessel wall.

CONCLUSIONS

Aneurysm initiation may be more likely in an area where both tensile forces acting on the vessel wall and total pressure loss are large.

Effects of Pulsatile Flow Rate and Shunt Ratio in Bifurcated Distal Arteries on Hemodynamic Characteristics Involved in Two Patient-Specific Internal Carotid Artery Sidewall Aneurysms: A Numerical Study

The pulsatile flow rate (PFR) in the cerebral artery system and shunt ratios in bifurcated arteries are two patient-specific parameters that may affect the hemodynamic characteristics in the pathobiology of cerebral aneurysms, which needs to be identified comprehensively. Accordingly, a systematic study was employed to study the effects of pulsatile flow rate (i.e., PFR−I, PFR−II, and PFR−III) and shunt ratio (i.e., 75:25 and 64:36) in bifurcated distal arteries, and transient cardiac pulsatile waveform on hemodynamic patterns in two internal carotid artery sidewall aneurysm models using computational fluid dynamics (CFD) modeling. Numerical results indicate that larger PFRs can cause higher wall shear stress (WSS) in some local regions of the aneurysmal dome that may increase the probability of small/secondary aneurysm generation than under smaller PFRs. The low WSS and relatively high oscillatory shear index (OSI) could appear under a smaller PFR, increasing the potential risk of aneurysmal sac growth and rupture. However, the variances in PFRs and bifurcated shunt ratios have rare impacts on the time-average pressure (TAP) distributions on the aneurysmal sac, although a higher PFR can contribute more to the pressure increase in the ICASA−1 dome due to the relatively stronger impingement by the redirected bloodstream than in ICASA−2. CFD simulations also show that the variances of shunt ratios in bifurcated distal arteries have rare impacts on the hemodynamic characteristics in the sacs, mainly because the bifurcated location is not close enough to the sac in present models. Furthermore, it has been found that the vortex location plays a major role in the temporal and spatial distribution of the WSS on the luminal wall, varying significantly with the cardiac period.

Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review

Aneurysms are malformations within the arterial vasculature brought on by the structural breakdown of the microarchitecture of the vessel wall, with aneurysms posing serious health risks in the event of their rupture. Blood flow within vessels is generally laminar with high, unidirectional wall shear stressors that modulate vascular endothelial cell functionality and regulate vascular smooth muscle cells. However, altered vascular geometry induced by bifurcations, significant curvature, stenosis, or clinical interventions can alter the flow, generating low stressor disturbed flow patterns. Disturbed flow is associated with altered cellular morphology, upregulated expression of proteins modulating inflammation, decreased regulation of vascular permeability, degraded extracellular matrix, and heightened cellular apoptosis. The understanding of the effects disturbed flow has on the cellular cascades which initiate aneurysms and promote their subsequent growth can further elucidate the nature of this complex pathology. This review summarizes the current knowledge about the disturbed flow and its relation to aneurysm pathology, the methods used to investigate these relations, as well as how such knowledge has impacted clinical treatment methodologies. This information can contribute to the understanding of the development, growth, and rupture of aneurysms and help develop novel research and aneurysmal treatment techniques.

Computational Assessment of Hemodynamics Vortices Within the Cerebral Vasculature Using Informational Entropy

Propper assessment of hemodynamic swirling flow patterns, vortices, may help understand the influence of disturbed flow on arterial wall pathophysiology and remodeling. Studies have shown that vortices trigger pathologic cellular changes within the vasculature such as increased inflammation and cellular apoptosis, leading to weakening of the vessel wall indicative of aneurysm development and rupture. Yet many studies qualitatively assess the presence of vortices within the vasculature or assess only their centermost region (critical point analysis) which overlooks the broader characteristics of flow, leading to a narrow view of vortices. This chapter provides a protocol for utilizing commercially available computational fluid dynamic software (ANSYS-FLUENT) to simulate realistic hemodynamic flow patterns, fluid velocity, and wall shear stress in the complex geometry of the cerebral vasculature, as well as an innovative method for assessing flow vortices. This innovative analytic methodology can identify areas of flow vortices and quantify how the broader bulk-flow (opposed to critical point) characteristics change in space and time over the cardiac cycle. Analysis of such flow structures can be used to identify specific characteristics such as vortex stability and the portion of an aneurysmal sac that is dominated by swirling flow, which may be indicative of vascular pathologies.

Quantitative analysis of flow vortices: differentiation of unruptured and ruptured medium-sized middle cerebral artery aneurysms

BACKGROUND

Surgical intervention for unruptured intracranial aneurysms (IAs) carries inherent health risks. The analysis of "patient-specific" IA geometric and computational fluid dynamics (CFD) simulated wall shear stress (WSS) data has been investigated to differentiate IAs at high and low risk of rupture to help clinical decision making. Yet, outcomes vary among studies, suggesting that novel analysis could improve rupture characterization. The authors describe a CFD analytic method to assess spatiotemporal characteristics of swirling flow vortices within IAs to improve characterization.

METHODS

CFD simulations were performed for 47 subjects harboring one medium-sized (4-10 mm) middle cerebral artery (MCA) aneurysm with available 3D digital subtraction angiography data. Alongside conventional indices, quantified IA flow vortex spatiotemporal characteristics were applied during statistical characterization. Statistical supervised machine learning using a support vector machine (SVM) method was run with cross-validation (100 iterations) to assess flow vortex-based metrics' strength toward rupture characterization.

RESULTS

Relying solely on vortex indices for statistical characterization underperformed compared with established geometric characteristics (total accuracy of 0.77 vs 0.80) yet showed improvements over wall shear stress models (0.74). However, the application of vortex spatiotemporal characteristics into the combined geometric and wall shear stress parameters augmented model strength for assessing the rupture status of middle cerebral artery aneurysms (0.85).

CONCLUSIONS

This preliminary study suggests that the spatiotemporal characteristics of flow vortices within MCA aneurysms are of value to improve the differentiation of ruptured aneurysms from unruptured ones.