Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation

Impact of workflow variability on image-based intracranial aneurysm hemodynamics

The interplay between intracranial aneurysm progression and hemodynamics motivates the application of image-based blood flow quantification, providing potential for the identification of high-risk aneurysms, treatment planning, and implant optimization. However, uncertainties arise throughout the interdisciplinary process, from medical imaging to parameter evaluation. This study systematically analyzes uncertainty globally, at individual workflow steps and for potential interactions. Eight factors affecting hemodynamic simulation accuracy - image reconstruction, lumen segmentation, surface smoothing, rheological modeling, inlet/outlet boundary condition, ostium/parent vessel definition - are varied for four representative patient-specific intracranial aneurysms. A total of 1024 transient simulations are evaluated considering twelve hemodynamic parameters to assess marginal and interaction effects. Global uncertainty analysis reveals median absolute deviations of 20.8-25.9 % for maximum velocity (Vmax), 6.8-19.2 % for inflow concentration index (ICI), 10.8-40.8 % for normalized wall shear stress (WSSnorm) and 2.8-48.9 % for low shear area (LSA). Isolated variation demonstrates the highest median deviations for the reconstruction algorithm (Vmax: 4.0-7.0 %, ICI: 6.8-18.9 %, WSSnorm: 13.3-25.1 %, LSA: 2.4-16.0 %), inlet (Vmax: 41.5-52.4 %, ICI: 1.4-8.6 %, WSSnorm: 14.6-28.5 %, LSA: 5.5-93.5 %) and outlet boundary condition (Vmax: 2.0-36.5 %, ICI: 0.6-39.9 %, WSSnorm: 2.4-83.2 %, LSA: 1.9-53.5 %). Lowest median deviations are found for rheological modeling and surface smoothing. Only minor interaction effects are observed between the reconstruction algorithm and inlet definition, as well as between inlet and outlet definitions. This study identifies pivotal variables essential for consistent hemodynamic quantification of intracranial aneurysms. Minimal interaction effects validate the isolated analysis of influencing factors in the majority of cases.

Feasibility Study for Multimodal Image-Based Assessment of Patient-Specific Intracranial Arteriovenous Malformation Hemodynamics

Background/Objectives: Intracranial arteriovenous malformations (AVMs) exhibit a complex vasculature characterized by a locally occurring tangled nidus connecting the arterial and venous system bypassing the capillary network. Clinically available imaging modalities may not give sufficient spatial or temporal resolution. Adequate 3D models of large vascular areas and a detailed blood flow analysis of the nidus including the surrounding vessels are not available yet. Methods: Three representative AVM cases containing multimodal image data (3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and phase-contrast quantitative magnetic resonance imaging) are investigated. Image segmentation results in partial 3D models of the different vascular segments, which are merged into large-scale neurovascular models. Subsequently, image-based blood flow simulations are conducted based on the segmented models using patient-specific flow measurements as boundary conditions. Results: The segmentation results provide comprehensive 3D models of the overall arteriovenous morphology including realistic nidus vessels. The qualitative results of the hemodynamic simulations show realistic flow behavior in the complex vasculature. Feeding arteries exhibit increased wall shear stress (WSS) and higher flow velocities in two cases compared to contralateral vessels. In addition, feeding arteries are exposed to higher overall WSS with increased value variation between individual vessels (20.1 Pa ± 17.3 Pa) compared to the draining veins having a 62% lower WSS (8.9 Pa ± 5.9 Pa). Blood flow distribution is dragged towards the dominating circulation side feeding the nidus for all the cases quantified by the volume flow direction changes in the posterior communicating arteries. Conclusions: This multimodal study demonstrates the feasibility of the presented workflow to acquire detailed blood flow predictions in large-scale AVM models based on complex image data. The hemodynamic models serve as a base for endovascular treatment modeling influencing flow patterns in distally located vasculatures.

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.

Intracranial aneurysm CTA images and 3D models dataset with clinical morphological and hemodynamic data

Intracranial aneurysm is a cerebrovascular disease associated with a high rupture risk, often resulting in death or severe disability. Recent advances in AI enable the prediction of intracranial aneurysm initiation, progression, and rupture through medical image analysis. Despite growing research interest, there is a shortage of publicly available datasets for training and validating AI models. This paper presents a comprehensive dataset comprising high-resolution CTA images of 99 patients with 105 MCA aneurysms and 44 normal healthy controls, along with their respective clinical data and 3D models of aneurysms and the parent arteries derived from the CTA images. Furthermore, recognizing the significance of blood hemodynamics on aneurysm development, this dataset also included the morphological and hemodynamic parameters obtained by computational fluid dynamics (CFD) for each patient and healthy control, which can be utilized by researchers without prior CFD experience. This dataset will facilitate hypothesis-driven or data-driven research on intracranial aneurysms, and has the potential to deepen our understanding of this disease.

On flow fluctuations in ruptured and unruptured intracranial aneurysms: resolved numerical study

Flow fluctuations have emerged as a promising hemodynamic metric for understanding of hemodynamics in intracranial aneurysms. Several investigations have reported flow instabilities using numerical tools. In this study, the occurrence of flow fluctuations is investigated using either Newtonian or non-Newtonian fluid models in five patient-specific intracranial aneurysms using high-resolution lattice Boltzmann simulation methods. Flow instabilities are quantified by computing power spectral density, proper orthogonal decomposition, and fluctuating kinetic energy of velocity fluctuations. Our simulations reveal substantial flow instabilities in two of the ruptured aneurysms, where the pulsatile inflow through the neck leads to hydrodynamic instability, particularly around the rupture position, throughout the entire cardiac cycle. In other monitoring points, the flow instability is primarily observed during the deceleration phase; typically, the fluctuations begin just after peak systole, gradually decay, and the flow returns to its original, laminar pulsatile state during diastole. Additionally, we assess the rheological impact on flow dynamics. The disparity between Newtonian and non-Newtonian outcomes remains minimal in unruptured aneurysms, with less than a 5% difference in key metrics. However, in ruptured cases, adopting a non-Newtonian model yields a substantial increase in the fluctuations within the aneurysm sac, with up to a 30% higher fluctuating kinetic energy compared to the Newtonian model. The study highlights the importance of using appropriate high-resolution simulations and non-Newtonian models to capture flow fluctuation characteristics that may be critical for assessing aneurysm rupture risk.

Credibility assessment of in silico clinical trials for medical devices

In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus 'synthetic' virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA's Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.

AnXplore: a comprehensive fluid-structure interaction study of 101 intracranial aneurysms

Advances in computational fluid dynamics continuously extend the comprehension of aneurysm growth and rupture, intending to assist physicians in devising effective treatment strategies. While most studies have first modelled intracranial aneurysm walls as fully rigid with a focus on understanding blood flow characteristics, some researchers further introduced Fluid-Structure Interaction (FSI) and reported notable haemodynamic alterations for a few aneurysm cases when considering wall compliance. In this work, we explore further this research direction by studying 101 intracranial sidewall aneurysms, emphasizing the differences between rigid and deformable-wall simulations. The proposed dataset along with simulation parameters are shared for the sake of reproducibility. A wide range of haemodynamic patterns has been statistically analyzed with a particular focus on the impact of the wall modelling choice. Notable deviations in flow characteristics and commonly employed risk indicators are reported, particularly with near-dome blood recirculations being significantly impacted by the pulsating dynamics of the walls. This leads to substantial fluctuations in the sac-averaged oscillatory shear index, ranging from -36% to +674% of the standard rigid-wall value. Going a step further, haemodynamics obtained when simulating a flow-diverter stent modelled in conjunction with FSI are showcased for the first time, revealing a 73% increase in systolic sac-average velocity for the compliant-wall setting compared to its rigid counterpart. This last finding demonstrates the decisive impact that FSI modelling can have in predicting treatment outcomes.

Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall

Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.

On the importance of fundamental computational fluid dynamics toward a robust and reliable model of left atrial flows

Computational fluid dynamics (CFD) studies of left atrial flows have reached a sophisticated level, for example, revealing plausible relationships between hemodynamics and stresses with atrial fibrillation. However, little focus has been on fundamental fluid modeling of LA flows. The purpose of this study was to investigate the spatiotemporal convergence, along with the differences between high- (HR) versus normal-resolution/accuracy (NR) solution strategies, respectively. Rigid wall CFD simulations were conducted on 12 patient-specific left atrial geometries obtained from computed tomography scans, utilizing a second-order accurate and space/time-centered solver. The convergence studies showed an average variability of around 30% and 55% for time averaged wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT), and endothelial cell activation potential (ECAP), even between intermediate spatial and temporal resolutions, in the left atrium (LA) and left atrial appendage (LAA), respectively. The comparison between HR and NR simulations showed good correlation in the LA for WSS, RRT, and ECAP (R 2 > .9 ), but not for OSI (R 2 = .63 ). However, there were poor correlations in the LAA especially for OSI, RRT, and ECAP (R 2 = .55, .63, and .61, respectively), except for WSS (R 2 = .81 ). The errors are comparable to differences previously reported with disease correlations. To robustly predict atrial hemodynamics and stresses, numerical resolutions of 10 M elements (i.e., Δ x = ∼ .5 mm) and 10 k time-steps per cycle seem necessary (i.e., one order of magnitude higher than normally used in both space and time). In conclusion, attention to fundamental numerical aspects is essential toward establishing a plausible, robust, and reliable model of LA flows.

The influence of blood velocity and vessel geometric parameters on wall shear stress

Vascular geometry was proposed to be one risk factor of atherosclerosis (AS). When developing this hypothesis, the discussion of geometry-wall shear stress (WSS) has often been included. However, further exploration on how various geometric parameters were affecting WSS was needed. The purpose of this study was to investigate the influence degree of vessel geometric parameters and blood velocity on WSS. A computational fluid dynamics (CFD) analyses of the vertebral and basilar arteries (VA and BA, respectively) was used. Twenty patients with no plaques or vessel wall thickening at the VA and BA were included. CFD analyses using both specific vessel models and flow conditions measured by ultrasound Doppler were performed. Subsequently, CFD results were post-processed with multiple linear regression to investigate numerical correlations between geometrical and flow parameters and WSS. The results of the multiple linear regression analysis further demonstrated that the BA proximal velocity was the most influential factor positively influencing BA WSS. The lower the WSS was, the stronger the influence brought by BA average diameter would be. The regression demonstrated that the contributions brought by average diameter and proximal velocity in lower WSS regions were lower than that in higher WSS regions. Tortuosity was only positively correlated with 97.5th WSS percentile, and vessel length and curvature showed no correlation with WSS. This study quantified the influence degree of BA morphology and flow velocity on WSS, which may have practical implications for predicting hemodynamic risks.

Image-based hemodynamic simulations for intracranial aneurysms: the impact of complex vasculature

PURPOSE

Hemodynamics play an important role in the assessment of intracranial aneurysm (IA) development and rupture risk. The purpose of this study was to examine the impact of complex vasculatures onto the intra-vessel and intra-aneurysmal blood flow.

METHODS

Complex segmentation of a subject-specific, 60-outlet and 3-inlet circle of Willis model captured with 7T magnetic resonance imaging was performed. This model was trimmed to a 10-outlet model version. Two patient-specific IAs were added onto both models yielding two pathological versions, and image-based blood flow simulations of the four resulting cases were carried out. To capture the differences between complex and trimmed model, time-averaged and centerline velocities were compared. The assessment of intra-saccular blood flow within the IAs involved the evaluation of wall shear stresses (WSS) at the IA wall and neck inflow rates (NIR).

RESULTS

Lower flow values are observed in the majority of the complex model. However, at specific locations (left middle cerebral artery 0.5 m/s, left posterior cerebral artery 0.25 m/s), higher flow rates were visible when compared to the trimmed counterpart. Furthermore, at the centerlines the total velocity values reveal differences up to 0.15 m/s. In the IAs, the reduction in the neck inflow rate and WSS in the complex model was observed for the first IA (IA-A δNIRmean =  - 0.07ml/s, PCA.l δWSSmean =  - 0.05 Pa). The second IA featured an increase in the neck inflow rate and WSS (IA-B δNIRmean = 0.04 ml/s, PCA.l δWSSmean = 0.07 Pa).

CONCLUSION

Both the magnitude and shape of the flow distribution vary depending on the model's complexity. The magnitude is primarily influenced by the global vessel model, while the shape is determined by the local structure. Furthermore, intra-aneurysmal flow strongly depends on the location in the vessel tree, emphasizing the need for complex model geometries for realistic hemodynamic assessment and rupture risk analysis.

Global tendencies and frontier topics in hemodynamics research of intracranial aneurysms: a bibliometric analysis from 1999 to 2022

Background: Hemodynamics plays a crucial role in the initiation, enlargement, and rupture of intracranial aneurysms (IAs). This bibliometric analysis aimed to map the knowledge network of IA hemodynamic research. Methods: Studies on hemodynamics in IAs published from 1999 to 2022 were retrieved from the Web of Science Core Collection (WoSCC). The contributions of countries, institutions, authors, and journals were identified using VOSviewer, Scimago Graphica, and Microsoft Excel. Tendencies, frontier topics, and knowledge networks were analyzed and visualized using VOSviewer and CiteSpace. Results: We identified 2,319 publications on hemodynamics in IAs. The annual number of publications exhibited an overall increasing trend. Among these, the United States, Japan, and China were the three major contributing countries. Capital Medical University, State University of New York (SUNY) Buffalo University, and George Mason University were the three most productive institutions. Meng H ranked first among authors regarding the number of articles and citations, while Cebral JR was first among co-cited authors. The American Journal of Neuroradiology was the top journal in terms of the number of publications, citations, and co-citations. In addition, the research topics can be divided into three clusters: hemodynamics itself, the relationship of hemodynamics with IA rupture, and the relationship of hemodynamics with IA treatment. The frontier directions included flow diverters, complications, morphology, prediction, recanalization, and four-dimensional flow magnetic resonance imaging (4D flow MRI). Conclusion: This study drew a knowledge map of the top countries, institutions, authors, publications, and journals on IA hemodynamics over the past 2 decades. The current and future hotspots of IA hemodynamics mainly include hemodynamics itself (4D flow MRI), its relationship with IA rupture (morphology and prediction), and its relationship with IA treatment (flow diverters, complications, and recanalization).

Is Accurate Lumen Segmentation More Important than Outlet Boundary Condition in Image-Based Blood Flow Simulations for Intracranial Aneurysms?

PURPOSE

Image-based blood flow simulations are increasingly used to investigate the hemodynamics in intracranial aneurysms (IAs). However, a strong variability in segmentation approaches as well as the absence of individualized boundary conditions (BCs) influence the quality of these simulation results leading to imprecision and decreased reliability. This study aims to analyze these influences on relevant hemodynamic parameters within IAs.

METHODS

As a follow-up study of an international multiple aneurysms challenge, the segmentation results of five IAs differing in size and location were investigated. Specifically, five possible outlet BCs were considered in each of the IAs. These are comprised of the zero-pressure condition (BC1), a flow distribution based on Murray's law with the exponents n = 2 (BC2) and n = 3 (BC3) as well as two advanced flow-splitting models considering the real vessels by including circular cross sections (BC4) or anatomical cross sections (BC5), respectively. In total, 120 time-dependent blood flow simulations were analyzed qualitatively and quantitatively, focusing on five representative intra-aneurysmal flow and five shear parameters such as vorticity and wall shear stress.

RESULTS

The outlet BC variation revealed substantial differences. Higher shear stresses (up to Δ9.69 Pa), intrasaccular velocities (up to Δ0.15 m/s) and vorticities (up to Δ629.22 1/s) were detected when advanced flow-splitting was applied compared to the widely used zero-pressure BC. The tendency of outlets BCs to over- or underestimate hemodynamic parameters is consistent across different segmentations of a single aneurysm model. Segmentation-induced variability reaches Δ19.58 Pa, Δ0.42 m/s and Δ957.27 1/s, respectively. Excluding low fidelity segmentations, however, (a) reduces the deviation drastically (>43%) and (b) leads to a lower impact of the outlet BC on hemodynamic predictions.

CONCLUSION

With a more realistic lumen segmentation, the influence of the BC on the resulting hemodynamics is decreased. A realistic lumen segmentation can be ensured, e.g., by using high-resolved 2D images. Furthermore, the selection of an advanced outflow-splitting model is advised and the use of a zero-pressure BC and BC based on Murray's law with exponent n = 3 should be avoided.

Computational Fluid Dynamics for the Prediction of Endograft Thrombosis in the Superficial Femoral Artery

Purpose:

Contemporary diagnostic modalities, including contrast-enhanced computed tomography (CTA) and duplex ultrasound, have been insufficiently able to predict endograft thrombosis. This study introduces an implementation of image-based computational fluid dynamics (CFD), by exemplification with 4 patients treated with an endograft for occlusive disease of the superficial femoral artery (SFA). The potential of personalized CFD for predicting endograft thrombosis is investigated.

Materials and Methods:

Four patients treated with endografts for an occluded SFA were retrospectively included. CFD simulations, based on CTA and duplex ultrasound, were compared for patients with and without endograft thrombosis to investigate potential flow-related causes of endograft thrombosis. Time-averaged wall shear stress (TAWSS) was computed, which highlights areas of prolonged residence times of coagulation factors in the graft.

Results:

CFD simulations demonstrated normal TAWSS (>0.4 Pa) in the SFA for cases 1 and 2, but low levels of TAWSS (<0.4 Pa) in cases 3 and 4, respectively. Primary patency was achieved in cases 1 and 2 for over 2 year follow-up. Cases 3 and 4 were complicated by recurrent endograft thrombosis.

Conclusion:

The presence of a low TAWSS was associated with recurrent endograft thrombosis in subjects with otherwise normal anatomic and ultrasound assessment and a good distal run-off.

Reproducibility of the computational fluid dynamic analysis of a cerebral aneurysm monitored over a decade

Computational fluid dynamics (CFD) simulations are increasingly utilised to evaluate intracranial aneurysm (IA) haemodynamics to aid in the prediction of morphological changes and rupture risk. However, these models vary and differences in published results warrant the investigation of IA-CFD reproducibility. This study aims to explore sources of intra-team variability and determine its impact on the aneurysm morphology and CFD parameters. A team of four operators were given six sets of magnetic resonance angiography data spanning a decade from one patient with a middle cerebral aneurysm. All operators were given the same protocol and software for model reconstruction and numerical analysis. The morphology and haemodynamics of the operator models were then compared. The segmentation, smoothing factor, inlet and outflow branch lengths were found to cause intra-team variability. There was 80% reproducibility in the time-averaged wall shear stress distribution among operators with the major difference attributed to the level of smoothing. Based on these findings, it was concluded that the clinical applicability of CFD simulations may be feasible if a standardised segmentation protocol is developed. Moreover, when analysing the aneurysm shape change over a decade, it was noted that the co-existence of positive and negative values of the wall shear stress divergence (WSSD) contributed to the growth of a daughter sac.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management

PURPOSE

Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine.

METHODS

We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification.

RESULTS

The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%.

CONCLUSION

The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework's performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/-2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.

Aneurysm Neck Overestimation has a Relatively Modest Impact on Simulated Hemodynamics

INTRODUCTION

Overestimation of intracranial aneurysm neck width by 3D angiography is a recognized clinical problem, and has long been a concern for image-based computational fluid dynamics (CFD). Recently, it was demonstrated that neck overestimation in 3D rotational angiography (3DRA) could be corrected via segmentation with upsampled resolution and gradient enhancement (SURGE). Our aim was to leverage this approach to determine whether and how neck overestimation actually impacts CFD-derived hemodynamics.

MATERIALS AND METHODS

A subset of 17 cases having the largest neck errors from a consecutive clinical sample of 60 was segmented from 3DRA using both standard watershed and SURGE methods. High-fidelity, pulsatile CFD was performed, and a variety of scalar hemodynamic parameters that have been associated with aneurysm growth and/or rupture status were derived.

RESULTS

With a few exceptions, flow and wall shear stress (WSS) patterns were qualitatively similar between neck-overestimated and corrected models. Sac-averaged WSS values were significantly lower after neck correction (p = 0.0005) but were highly correlated with their neck-overestimated counterparts (R² = 0.98). Jet impingement was significantly more concentrated in the neck-corrected vs. -uncorrected models (p = 0.0011), and only moderately correlated (R² = 0.61). Parameters quantifying velocity or WSS fluctuations were not significantly different after neck correction, but this reflected their poorer correlations (R² < 0.4). Nevertheless, for all hemodynamic parameters, median absolute differences were < 26%, and no parameter had more than 5/17 cases with absolute differences > 50%.

CONCLUSION

Differences in hemodynamics due to neck width overestimation were found to be at most equal to, and often less than, those reported for other sources of error/uncertainty in intracranial aneurysm CFD, such as solver settings or assumed inflow rates.

High-fidelity fluid structure interaction simulations of turbulent-like aneurysm flows reveals high-frequency narrowband wall vibrations: A stimulus of mechanobiological relevance?

Recent high-fidelity/resolution computational fluid dynamics simulations of intracranial aneurysm hemodynamics have revealed turbulent-like flows. We hypothesized that the associated high-frequency pressure fluctuations could promote aneurysm wall vibrations. We performed fully coupled high-fidelity transient fluid structure interaction simulations between the blood flow and compliant aneurysm sac wall taking 5,000 time steps per second using a 3D patient-specific model previously shown to harbour turbulent-like flow. Our results show that the flow velocity contained fluctuations with a smooth and continuously decaying energy up to ∼160Hz, and fluctuating pressures with characteristic frequency peaks at approximately 30, 130 and 210Hz. There was a strong two-way coupling between the pressure and the wall deformation, for which the frequency spectrum showed similar characteristics, but with a narrow band peak at ∼120Hz with large regional differences in amplitude up to 80μm. The physics of the flow is broadly consistent with clinical reports of turbulent-like flows, while the physics of the wall is consistent with reports of spectral peaks in aneurysm patients. As many aneurysms are known to harbour turbulent-like flows, wall vibrations could be a widespread phenomenon. Finally, since aneurysms are vascular pathologies by definition and many/most aneurysms do not have endothelial cells but still display a focal remodeling, we hypothesize that vibrations and stresses within the wall itself might play a role in the mechanobiological processes of vessel wall pathology.

Geometric uncertainty in intracranial aneurysm rupture status discrimination: a two-site retrospective study

OBJECTIVES

Assessing the risk associated with unruptured intracranial aneurysms (IAs) is essential in clinical decision making. Several geometric risk parameters have been proposed for this purpose. However, performance of these parameters has been inconsistent. This study evaluates the performance and robustness of geometric risk parameters on two datasets and compare it to the uncertainty inherent in assessing these parameters and quantifies interparameter correlations.

METHODS

Two datasets containing 244 ruptured and unruptured IA geometries from 178 patients were retrospectively analysed. IAs were stratified by anatomical region, based on the PHASES score locations. 37 geometric risk parameters representing four groups (size, neck, non-dimensional, and curvature parameters) were assessed. Analysis included standardised absolute group differences (SADs) between ruptured and unruptured IAs, ratios of SAD to median relative uncertainty (MRU) associated with the parameters, and interparameter correlation.

RESULTS

The ratio of SAD to MRU was lower for higher dimensional size parameters (ie, areas and volumes) than for one-dimensional size parameters. Non-dimensional size parameters performed comparatively well with regard to SAD and MRU. SAD was higher in the posterior anatomical region. Correlation of parameters was strongest within parameter (sub)groups and between size and curvature parameters, while anatomical region did not strongly affect correlation patterns.

CONCLUSION

Non-dimensional parameters and few parameters from other groups were comparatively robust, suggesting that they might generalise better to other datasets. The data on discriminative performance and interparameter correlations presented in this study may aid in developing and choosing robust geometric parameters for use in rupture risk models.

Challenges in Modeling Hemodynamics in Cerebral Aneurysms Related to Arteriovenous Malformations

PURPOSE

The significantly higher incidence of aneurysms in patients with arteriovenous malformations (AVMs) suggests a strong hemodynamic relationship between these lesions. The presence of an AVM alters hemodynamics in proximal vessels by drastically changing the distal resistance, thus affecting intra-aneurysmal flow. This study discusses the challenges associated with patient-specific modeling of aneurysms in the presence of AVMs.

METHODS

We explore how the presence of a generic distal AVM affects upstream aneurysms by examining the relationship between distal resistance and aneurysmal wall shear stress using physiologically realistic estimates for the influence of the AVM on hemodynamics. Using image-based computational models of aneurysms and surrounding vasculature, aneurysmal wall-shear stress is calculated for a range of distal resistances corresponding to the presence of AVMs of various sizes and compared with a control case representing the absence of an AVM.

RESULTS

In the patient cases considered, the alteration in aneurysmal wall shear stress due to the presence of an AVM is considerable, as much as 19 times the base case wall shear stress. Furthermore, the relationship between aneurysmal wall shear stress and distal resistance is shown to be highly geometry-dependent and nonlinear. In most cases, the range of physiologically realistic possibilities for AVM-related distal resistance are so large that patient-specific flow measurements are necessary for meaningful predictions of wall shear stress.

CONCLUSIONS

The presented work offers insight on the impact of distal AVMs on aneurysmal wall shear stress using physiologically realistic computational models. Patient-specific modeling of hemodynamics in aneurysms and associated AVMs has great potential for understanding lesion pathogenesis, surgical planning, and assessing the effect of treatment of one lesion relative to another. However, we show that modeling approaches cannot usually meaningfully quantify the impact of AVMs if based solely on imaging data from CT and X-ray angiography, currently used in clinical practice. Based on recent studies, it appears that 4D flow MRI is one promising approach to obtaining meaningful patient-specific flow boundary conditions that improve modeling fidelity.

Improving visualization of three-dimensional aneurysm features via segmentation with upsampled resolution and gradient enhancement (SURGE)

BACKGROUND

Intracranial aneurysm neck width tends to be overestimated when measured with three-dimensional rotational angiography (3DRA) compared with two-dimensional digital subtraction angiography (2D-DSA), owing to high curvature at the neck. This may affect morphological and hemodynamic analysis in support of treatment planning. We present and validate a method for extracting high curvature features, such as aneurysm ostia, during segmentation of 3DRA images.

METHODS

In our novel SURGE (segmentation with upsampled resolution and gradient enhancement) approach, the gradient of an upsampled image is sharpened before gradient-based watershed segmentation. Neck measurements were performed for both standard and SURGE segmentations of 3DRA for 60 consecutive patients and compared with those from 2D-DSA. Those segmentations were also qualitatively compared for surface topology and morphology.

RESULTS

Compared with the standard watershed method, SURGE reduced neck measurement error relative to 2D-DSA by >60%: median error was 0.49 mm versus 0.17 mm for SURGE, which is less than the average pixel resolution (~0.33 mm) of the 3DRA dataset. SURGE reduced neck width overestimations >1 mm from 13/60 to 5/60 cases. Relative to 2D-DSA, standard segmentations were overestimated by 16% and 93% at median and 95th percentiles, respectively, compared with only 6% and 37%, respectively, for SURGE.

CONCLUSION

SURGE provides operators with high-level control of the image gradient, allowing recovery of high-curvature features such as aneurysm ostia from 3DRA where conventional algorithms may fail. Compared with standard segmentation and tedious manual editing, SURGE provides a faster, easier, and more objective method for assessing aneurysm ostia and morphology.

Blood flow analysis with computational fluid dynamics and 4D-flow MRI for vascular diseases

Both computational fluid dynamics (CFD) and time-resolved, three-dimensional, phase-contrast, magnetic resonance imaging (4D-flow MRI) enable visualization of time-varying blood flow structures and quantification of blood flow in vascular diseases. However, they are totally different. CFD is a method to calculate blood flow by solving the governing equations of fluid mechanics, so the obtained flow field is somewhat virtual. On the other hand, 4D-flow MRI measures blood flow in vivo, thus the flow is real. Recently, with the development and enhancement of computers, medical imaging techniques, and related software, blood flow analysis has become more accessible to clinicians and its usefulness in vascular diseases has been demonstrated. In this review, we have outlined the methods and characteristics of CFD and 4D-flow MRI, respectively. We have discussed the differences in the characteristics between both methods; reviewed the milestones achieved by blood flow analysis in various vascular diseases; and discussed the usefulness, challenges, and limitations of blood flow analysis. We have discussed the difficulties and limitations of current blood flow analysis. We have also discussed our views on future directions.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Computational Fluid Dynamics for the Prediction of Endograft Thrombosis in the Superficial Femoral Artery

PURPOSE

Contemporary diagnostic modalities, including contrast-enhanced computed tomography (CTA) and duplex ultrasound, have been insufficiently able to predict endograft thrombosis. This study introduces an implementation of image-based computational fluid dynamics (CFD), by exemplification with 4 patients treated with an endograft for occlusive disease of the superficial femoral artery (SFA). The potential of personalized CFD for predicting endograft thrombosis is investigated.

MATERIALS AND METHODS

Four patients treated with endografts for an occluded SFA were retrospectively included. CFD simulations, based on CTA and duplex ultrasound, were compared for patients with and without endograft thrombosis to investigate potential flow-related causes of endograft thrombosis. Time-averaged wall shear stress (TAWSS) was computed, which highlights areas of prolonged residence times of coagulation factors in the graft.

RESULTS

CFD simulations demonstrated normal TAWSS (>0.4 Pa) in the SFA for cases 1 and 2, but low levels of TAWSS (<0.4 Pa) in cases 3 and 4, respectively. Primary patency was achieved in cases 1 and 2 for over 2 year follow-up. Cases 3 and 4 were complicated by recurrent endograft thrombosis.

CONCLUSION

The presence of a low TAWSS was associated with recurrent endograft thrombosis in subjects with otherwise normal anatomic and ultrasound assessment and a good distal run-off.

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery

Automated tools for landmarking the internal carotid artery (ICA) bends have the potential for efficient and objective medical image-based morphometric analysis. The two existing algorithms rely on numerical approximations of curvature and torsion of the centerline. However, input parameters, original source code, comparability, and robustness of the algorithms remain unknown. To address the former two, we have re-implemented the algorithms, followed by sensitivity analyses. Of the input parameters, the centerline smoothing had the least impact resulting in 6-7 bends, which is anatomically realistic. In contrast, centerline resolution showed to completely over- and underestimated the number of bends varying from 3 to 33. Applying the algorithms to the same cohort revealed a variability that makes comparison of results between previous studies questionable. Assessment of robustness revealed how one algorithm is vulnerable to model smoothness and noise, but conceptually independent of application. In contrast, the other algorithm is robust and consistent, but with limited general applicability. In conclusion, both algorithms are equally valid albeit they produce vastly different results. We have provided a well-documented open-source implementation of the algorithms. Finally, we have successfully performed this study on the ICA, but application to other vascular regions should be performed with caution.

On the prevalence of flow instabilities from high-fidelity computational fluid dynamics of intracranial bifurcation aneurysms

High-fidelity computational fluid dynamics (HF-CFD) has revealed the potential for high-frequency flow instabilities (aka "turbulent-like" flow) in intracranial aneurysms, consistent with classic in vivo and in vitro reports of bruits and/or wall vibrations. However, HF-CFD has typically been performed on limited numbers of cases, often with unphysiological inflow conditions or focused on sidewall-type aneurysms where flow instabilities may be inherently less prevalent. Here we report HF-CFD of 50 bifurcation aneurysm cases from the open-source Aneurisk model repository. These were meshed using quadratic finite elements having an average effective spatial resolution of 0.065 mm, and solved under physiologically-pulsatile flow conditions using a well-validated, minimally-dissipative solver with 20,000 time-steps per cardiac cycle Flow instability was quantified using the recently introduced spectral power index (SPI), which quantifies, from 0 to 1, the power associated with velocity fluctuations above those of the driving inflow waveform. Of the 50 cases, nearly half showed regions within the sac having SPI up to 0.5, often with non-negligible power into the 100's of Hz, and roughly 1/3 had sac-averaged SPI > 0.1. High SPI did not significantly predict rupture status in this cohort. Proper orthogonal decomposition of cases with highest SPI_avg revealed time-varying energetics consistent with transient turbulence. Our reported prevalence of high-frequency flow instabilities in HF-CFD modelling of aneurysms suggests that care must be taken to avoid routinely overlooking them if we are to understand the highly dynamic mechanical forces to which some aneurysm walls may be exposed, and their prevalence in vivo.

Identification of intra-individual variation in intracranial arterial flow by MRI and the effect on computed hemodynamic descriptors

OBJECTIVES

To determine the intra-individual flow variation in serially acquired studies, and the influence of this variation on subsequent hemodynamic simulations using the inlet flow as a boundary condition. Author: Kindly check and confirm whether the corresponding authors are correctly identified.Confirmed.

MATERIALS AND METHODS

This prospective study included 51 patients (37 females and 14 males) with unruptured intracranial aneurysms who have received more than three times follow-up of 2D phase-contrast MR. The flow and velocity parameters were extracted to calculate the reproducibility and variation. Patient-specific computational fluid dynamics simulations were performed using the measured flows.

RESULTS

Intraclass correlation coefficients for mean and maximum velocity and flow parameters ranged from 0.77 to 0.90. A 10% CV of mean flow was identified. Variations of 10% in inlet flow resulted in hemodynamic changes including 41.41% of peak systolic wall shear stress; 39.13% of end-diastolic wall shear stress; 2.79% of low shear area at peak systole; 2.12% of low shear area at end diastole: 47.57% of time-averaged wall shear stress; and 0.17% of oscillatory shear index.

CONCLUSION

This study identified 10% of intra-individual mean flow variation on phase-contrast MR. Intra-individual flow variation resulted in a non-negligible variation in wall shear stress, but relatively small variation in low shear area in hemodynamic calculations.

Correlation of Inflow Velocity Ratio Detected by Phase Contrast Magnetic Resonance Angiography with the Bleb Color of Unruptured Intracranial Aneurysms

BACKGROUND

Intraoperative rupture is the most fatal and catastrophic complication of surgery for unruptured intracranial aneurysms (UIAs); thus, it is extremely useful to predict reddish and thin-walled regions of the UIA before surgery. Although several studies have reported a relationship between the hemodynamic characteristics and intracranial aneurysm wall thickness, a consistent opinion is lacking. We aimed to investigate the relationship between objectively and quantitatively evaluated bleb wall color and hemodynamic characteristics using phase-contrast magnetic resonance angiography (PC-MRA).

METHODS

Ten patients diagnosed with UIA who underwent surgical clipping and preoperative magnetic resonance imaging along with PC-MRA were included in this study. Bleb wall color was evaluated from an intraoperative video. Based on the Red (R), Green, and Blue values, bleb wall redness (modified R value; mR) was calculated and compared with the hemodynamic characteristics obtained from PC-MRA.

RESULTS

The wall redness distribution of 18 blebs in 11 UIAs in 10 patients was analyzed. Bleb/neck inflow velocity ratio (Vb/Va: r = 0.66, P = 0.003) strongly correlated with mR, whereas bleb/neck inflow rate ratio (r = 0.58, P = 0.012) correlated moderately. Multivariate regression analysis revealed that only Vb/Va (P = 0.017) significantly correlated with mR. There was no correlation between wall shear stress and mR.

CONCLUSIONS

The bleb redness of UIAs and Vb/Va, calculated using PC-MRA, showed a significantly greater correlation. Thus, it is possible to predict bleb thickness noninvasively before surgery. This will facilitate more detailed pre- and intraoperative strategies for clipping and coiling for safe surgery.

Application of physics-based flow models in cardiovascular medicine: Current practices and challenges

Personalized physics-based flow models are becoming increasingly important in cardiovascular medicine. They are a powerful complement to traditional methods of clinical decision-making and offer a wealth of physiological information beyond conventional anatomic viewing using medical imaging data. These models have been used to identify key hemodynamic biomarkers, such as pressure gradient and wall shear stress, which are associated with determining the functional severity of cardiovascular diseases. Importantly, simulation-driven diagnostics can help researchers understand the complex interplay between geometric and fluid dynamic parameters, which can ultimately improve patient outcomes and treatment planning. The possibility to compute and predict diagnostic variables and hemodynamics biomarkers can therefore play a pivotal role in reducing adverse treatment outcomes and accelerate development of novel strategies for cardiovascular disease management.

3D-printed, patient-specific intracranial aneurysm models: From clinical data to flow experiments with endovascular devices

PURPOSE

Flow models of intracranial aneurysms (IAs) can be used to test new and existing endovascular treatments with flow modulation devices (FMDs). Additionally, 4D flow magnetic resonance imaging (MRI) offers the ability to measure hemodynamics. This way, the effect of FMDs can be determined noninvasively and compared to patient data. Here, we describe a cost-effective method for producing flow models to test the efficiency of FMDs with 4D flow MRI.

METHODS

The models were based on human radiological data (internal carotid and basilar arteries) and printed in 3D with stereolithography. The models were printed with three different printing layers (25, 50, and 100 µm thickness). To evaluate the models in vitro, 3D rotational angiography, time-of-flight MRI, and 4D flow MRI were employed. The flow and geometry of one model were compared with in vivo data. Two FMDs (FMD1 and FMD2) were deployed into two different IA models, and the effect on the flow was estimated by 4D flow MRI.

RESULTS

Models printed with different layer thicknesses exhibited similar flow and little geometric variation. The mean spatial difference between the vessel geometry measured in vivo and in vitro was 0.7 ± 1.1 mm. The main flow features, such as vortices in the IAs, were reproduced. The velocities in the aneurysms were similar in vivo and in vitro (mean velocity magnitude: 5.4 ± 7.6 and 7.7 ± 8.6 cm/s, maximum velocity magnitude: 72.5 and 55.1 cm/s). By deploying FMDs, the mean velocity was reduced in the IAs (from 8.3 ± 10 to 4.3 ± 9.32 cm/s for FMD1 and 9.9 ± 12.1 to 2.1 ± 5.6 cm/s for FMD2).

CONCLUSIONS

The presented method allows to produce neurovascular models in approx. 15 to 30 h. The resulting models were found to be geometrically accurate, reproducing the main flow patterns, and suitable for implanting FMDs as well as 4D flow MRI.

Association between aneurysm hemodynamics and wall enhancement on 3D vessel wall MRI

OBJECTIVE

Aneurysm wall enhancement (AWE) on 3D vessel wall MRI (VWMRI) has been suggested as an imaging biomarker for intracranial aneurysms (IAs) at higher risk of rupture. While computational fluid dynamics (CFD) studies have been used to investigate the association between hemodynamic forces and rupture status of IAs, the role of hemodynamic forces in unruptured IAs with AWE is poorly understood. The authors investigated the role and implications of abnormal hemodynamics related to aneurysm pathophysiology in patients with AWE in unruptured IAs.

METHODS

Twenty-five patients who had undergone digital subtraction angiography (DSA) and VWMRI studies from September 2016 to September 2017 were included, resulting in 22 patients with 25 IAs, 9 with and 16 without AWE. High-resolution CFD models of hemodynamics were created from DSA images. Univariate and multivariate analyses were performed to investigate the association between AWE and conventional morphological and hemodynamic parameters. Normalized MRI signal intensity was quantified and quantitatively associated with wall shear stresses (WSSs) for the entire aneurysm sac, and in regions of low, intermediate, and high WSS.

RESULTS

The AWE group had lower WSS (p < 0.01) and sac-averaged velocity (p < 0.01) and larger aneurysm size (p < 0.001) and size ratio (p = 0.0251) than the non-AWE group. From multivariate analysis of both hemodynamic and morphological factors, only low WSS was found to be independently associated with AWE. Sac-averaged normalized MRI signal intensity correlated with WSS and was significantly different in regions of low WSS compared to regions of intermediate (p = 0.018) and high (p < 0.001) WSS.

CONCLUSIONS

The presence of AWE was associated with morphological and hemodynamic factors related to rupture risk. Low WSS was found to be an independent predictor of AWE. Our findings support the hypothesis that low WSS in IAs with AWE may indicate a growth and remodeling process that may predispose such aneurysms to rupture; however, a causality between the two cannot be established.

Associations between morphology and hemodynamics of intracranial aneurysms based on 4D flow and black-blood magnetic resonance imaging

Background

Previous studies have hypothesized that intracranial aneurysm (IA) morphology interacts with hemodynamic conditions. Magnetic resonance imaging (MRI) provides a single image modality solution for both morphological and hemodynamic measurements for IA. This study aimed to explore the interaction between the morphology and hemodynamics of IA using black-blood MRI (BB-MRI) and 4D flow MRI.

Methods

A total of 97 patients with unruptured IA were recruited for this study. The IA size, size ratio (SR), and minimum wall thickness (mWT) were measured using BB-MRI. Velocity, blood flow, pulsatility index (PI), and wall shear stress (WSS) were measured with 4D flow MRI. The relationship between hemodynamic parameters and morphological indices was investigated by linear regression analysis and unpaired two-sample t-test. To determine the independent interaction, multiple linear regression analysis was further performed.

Results

The findings showed that mWT was negatively correlated with IA size (r=-0.665, P<0.001). Maximum blood flow in IA (Flow_IA) was positively correlated with IA size (r=0.458, P<0.001). The average WSS (WSS_avg) was negatively correlated with IA size (r=-0.650, P<0.001). The relationships remained the same after the multivariate analysis was adjusted for hemodynamic, morphologic, and demographic confounding factors. The WSS_avg was positively correlated with mWT (r=0.528, P<0.001). In the unpaired two-sample t-test, mWT, WSS_avg, and Flow_IA were statistically significantly associated with the size and SR of IAs.

Conclusions

There is potential for BB-MRI and 4D flow MRI to provide morphological and hemodynamic information regarding IA. Blood flow, WSS, and mWT may serve as non-invasive biomarkers for IA assessments, and may contribute to a more comprehensive understanding of the mechanism of IA.

Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI

PURPOSE

Variations in the vessel radius of segmented surfaces of intracranial aneurysms significantly influence the fluid velocities given by computer simulations. It is important to generate models that capture the effect of these variations in order to have a better interpretation of the numerically predicted hemodynamics. Also, it is highly relevant to develop methods that combine experimental observations with uncertainty modeling to get a closer approximation to the blood flow behavior.

METHODS

This work applies polynomial chaos expansion to model the effect of geometric uncertainties on the simulated fluid velocities of intracranial aneurysms. The radius of the vessel is defined as the uncertainty variable. Proper orthogonal decomposition is applied to characterize the solution space of fluid velocities. Next, a process of projecting the 4D-Flow MRI velocities on the basis vectors followed by coefficient mapping using generalized dynamic mode decomposition enables the merging of 4D-Flow MRI with the uncertainty propagated fluid velocities.

RESULTS

Polynomial chaos expansion propagates the fluid velocities with an error of 2% in velocity magnitude relative to computer simulations. Also, the bifurcation region (or impingement location) shows a standard deviation of 0.17 m/s (since an available reported variance in the vessel radius is adopted to model the uncertainty, the expected standard deviation may be different). Numerical phantom experiments indicate that the proposed approach reconstructs the fluid velocities with 0.3% relative error in presence of geometric uncertainties.

CONCLUSION

Polynomial chaos expansion is an effective approach to propagate the effect of the uncertainty variable in the blood flow velocities of intracranial aneurysms. Merging 4D-Flow MRI and uncertainty propagated fluid velocities leads to more realistic flow trends relative to ignoring the uncertainty in the vessel radius.

Integrated Image-Based Computational Fluid Dynamics Modeling Software as an Instructional Tool

Computational modeling of cardiovascular flows is becoming increasingly important in a range of biomedical applications, and understanding the fundamentals of computational modeling is important for engineering students. In addition to their purpose as research tools, integrated image-based computational fluid dynamics (CFD) platforms can be used to teach the fundamental principles involved in computational modeling and generate interest in studying cardiovascular disease. We report the results of a study performed at five institutions designed to investigate the effectiveness of an integrated modeling platform as an instructional tool and describe "best practices" for using an integrated modeling platform in the classroom. Use of an integrated modeling platform as an instructional tool in nontraditional educational settings (workshops, study abroad programs, in outreach) is also discussed. Results of the study show statistically significant improvements in understanding after using the integrated modeling platform, suggesting such platforms can be effective tools for teaching fundamental cardiovascular computational modeling principles.

How patient specific are patient-specific computational models of cerebral aneurysms? An overview of sources of error and variability

Computational modeling of cerebral aneurysms, derived from clinical 3D angiography, has become widespread over the past 15 years. While such "image-based" or "patient-specific" models have shown promise for the assessment of rupture risk, much debate remains about their reliability in light of necessary modeling assumptions and incomplete or uncertain model input parameters derived from the clinic. The aims of this review were to walk through the various steps of this so-called patient-specific modeling pipeline and to highlight evidence supporting those steps that we can or cannot rely on. The relative importance of the different sources of error and variability on hemodynamic predictions is summarized, with recommendations to standardize for those that can be avoided and to pay closer attention those to that cannot.

A review on the reliability of hemodynamic modeling in intracranial aneurysms: why computational fluid dynamics alone cannot solve the equation

Computational blood flow modeling in intracranial aneurysms (IAs) has enormous potential for the assessment of highly resolved hemodynamics and derived wall stresses. This results in an improved knowledge in important research fields, such as rupture risk assessment and treatment optimization. However, due to the requirement of assumptions and simplifications, its applicability in a clinical context remains limited.This review article focuses on the main aspects along the interdisciplinary modeling chain and highlights the circumstance that computational fluid dynamics (CFD) simulations are embedded in a multiprocess workflow. These aspects include imaging-related steps, the setup of realistic hemodynamic simulations, and the analysis of multidimensional computational results. To condense the broad knowledge, specific recommendations are provided at the end of each subsection.Overall, various individual substudies exist in the literature that have evaluated relevant technical aspects. In this regard, the importance of precise vessel segmentations for the simulation outcome is emphasized. Furthermore, the accuracy of the computational model strongly depends on the specific research question. Additionally, standardization in the context of flow analysis is required to enable an objective comparison of research findings and to avoid confusion within the medical community. Finally, uncertainty quantification and validation studies should always accompany numerical investigations.In conclusion, this review aims for an improved awareness among physicians regarding potential sources of error in hemodynamic modeling for IAs. Although CFD is a powerful methodology, it cannot provide reliable information, if pre- and postsimulation steps are inaccurately carried out. From this, future studies can be critically evaluated and real benefits can be differentiated from results that have been acquired based on technically inaccurate procedures.

Intracranial aneurysm growth: consistency of morphological changes

OBJECTIVE

Previous studies have shown a relation between growth and rupture of intracranial aneurysms. Additionally, several morphological characteristics are frequently measured to estimate rupture risk. Little is known about how the rupture risk is associated with morphological characteristic changes during growth. The aim of this study was to provide insights into how morphological characteristics, associated with rupture, change during an aneurysm's growth.

METHODS

The authors retrospectively identified patients with longitudinal MRA images of unruptured growing aneurysms. The MRA images had an in-plane resolution of 0.2-0.5 mm and a slice thickness of 0.2-0.75 mm. Therefore, growth was defined as an increase of at least 0.5 mm in two directions or 1 mm in one direction. Using the MRA images, the authors semiautomatically segmented the aneurysm and the perianeurysmal vasculature. Twelve morphological characteristics were automatically measured. These characteristics were related to size (diameter, height, width, neck diameter, volume, surface area, aspect ratio, height-width ratio, and bottleneck factor) and shape (ellipticity index, nonsphericity index, and undulation index) of the aneurysm. Morphological characteristics before and after growth were compared using the Wilcoxon signed-rank test.

RESULTS

The authors included 31 patients with 38 growing aneurysms. The aneurysms' growth was detected after a mean of 218 weeks (range 23-567 weeks). A significant increase was seen in all size-related characteristics, and the bottleneck factor also significantly increased (from a median of 1.00 [IQR 0.85-1.04] to 1.03 [IQR 0.93-1.18]), while the ellipticity index decreased (from a median of 0.26 [IQR 0.25-0.28] to 0.25 [IQR 0.24-0.26]). The changes in size ratios and shape indices varied largely among patients. Larger aneurysms more often showed an increase in shape ratios.

CONCLUSIONS

Although aneurysm growth, size-related characteristics, bottleneck factor, and ellipticity index changed significantly during growth, most size ratios and shape indices showed inconsistent changes among aneurysms. This suggests that, for an accurate rupture prediction, morphological parameters need to be reassessed after growth.

Computational fluid dynamics as a risk assessment tool for aneurysm rupture

OBJECTIVE

The authors reviewed the clinical role of computational fluid dynamics (CFD) in assessing the risk of intracranial aneurysm rupture.

METHODS

A literature review was performed to identify reports on CFD assessment of aneurysms using PubMed. The usefulness of various hemodynamic parameters, such as wall shear stress (WSS) and the Oscillatory Shear Index (OSI), and their role in aneurysm rupture risk analysis, were analyzed.

RESULTS

The authors identified a total of 258 published articles evaluating rupture risk, growth, and endovascular device assessment. Of these 258 articles, 113 matching for CFD and hemodynamic parameters that contribute to the risk of rupture (such as WSS and OSI) were identified. However, due to a lack of standardized methodology, controversy remains on each parameter's role.

CONCLUSIONS

Although controversy continues to exist on which risk factors contribute to predict aneurysm rupture, CFD can provide additional parameters to assess this rupture risk. This technology can contribute to clinical decision-making or evaluation of efficacy for endovascular methods and devices.

How patient-specific do internal carotid artery inflow rates need to be for computational fluid dynamics of cerebral aneurysms?

BACKGROUND

Computational fluid dynamics (CFD) has become a popular tool for studying 'patient-specific' blood flow dynamics in cerebral aneurysms; however, rarely are the inflow boundary conditions patient-specific. We aimed to test the impact of widespread reliance on generalized inflow rates.

METHODS

Internal carotid artery (ICA) flow rates were measured via 2D cine phase-contrast MRI for 24 patients scheduled for endovascular therapy of an ICA aneurysm. CFD models were constructed from 3D rotational angiography, and pulsatile inflow rates imposed as measured by MRI or estimated using an average older-adult ICA flow waveform shape scaled by a cycle-average flow rate (Q_avg) derived from the patient's ICA cross-sectional area via an assumed inlet velocity.

RESULTS

There was good overall qualitative agreement in the magnitudes and spatial distributions of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and spectral power index (SPI) using generalized versus patient-specific inflows. Sac-averaged quantities showed moderate to good correlations: R²=0.54 (TAWSS), 0.80 (OSI), and 0.68 (SPI). Using patient-specific Q_avg to scale the generalized waveform shape resulted in near-perfect agreement for TAWSS, and reduced bias, but not scatter, for SPI. Patient-specific waveform had an impact only on OSI correlations, which improved to R²=0.93.

CONCLUSIONS

Aneurysm CFD demonstrates the ability to stratify cases by nominal hemodynamic 'risk' factors when employing an age- and vascular-territory-specific recipe for generalized inflow rates. Q_avg has a greater influence than waveform shape, suggesting some improvement could be achieved by including measurement of patient-specific Q_avg into aneurysm imaging protocols.

Multimodal validation of focal enhancement in intracranial aneurysms as a surrogate marker for aneurysm instability

Purpose

Circumferential enhancement on MR vessel wall imaging has been proposed as a biomarker of a higher risk of rupture in intracranial aneurysms. Focal enhancement is frequently encountered in unruptured aneurysms, but its implication for risk stratification and patient management remains unclear. This study investigates the association of focal wall enhancement with hemodynamic and morphological risk factors and histologic markers of wall inflammation and degeneration.

Methods

Patients with an unruptured middle cerebral artery aneurysm who underwent 3D rotational angiography and 3T MR vessel wall imaging showing focal wall enhancement were included. Hemodynamic parameters were calculated based on flow simulations and compared between enhanced regions and the entire aneurysm surface. Morphological parameters were semiautomatically extracted and quantitatively associated with wall enhancement. Histological analysis included detection of vasa vasorum, CD34, and myeloperoxidase staining in a subset of patients.

Results

Twenty-two aneurysms were analyzed. Enhanced regions were significantly associated with lower AWSS, lower maxOSI, and increased LSA. In multivariate analysis, higher ellipticity index was an independent predictor of wall enhancement. Histologic signs of inflammation and degeneration and higher PHASES score were significantly associated with focal enhancement.

Conclusion

Focal wall enhancement is colocalized with hemodynamic factors that have been related to a higher rupture risk. It is correlated with morphological factors linked to rupture risk, higher PHASES score, and histologic markers of wall destabilization. The results support the hypothesis that focal enhancement could serve as a surrogate marker for aneurysm instability.

An Automated Workflow for Hemodynamic Computations in Cerebral Aneurysms

In recent years, computational fluid dynamics (CFD) has become a valuable tool for investigating hemodynamics in cerebral aneurysms. CFD provides flow-related quantities, which have been shown to have a potential impact on aneurysm growth and risk of rupture. However, the adoption of CFD tools in clinical settings is currently limited by the high computational cost and the engineering expertise required for employing these tools, e.g., for mesh generation, appropriate choice of spatial and temporal resolution, and of boundary conditions. Herein, we address these challenges by introducing a practical and robust methodology, focusing on computational performance and minimizing user interaction through automated parameter selection. We propose a fully automated pipeline that covers the steps from a patient-specific anatomical model to results, based on a fast, graphics processing unit- (GPU-) accelerated CFD solver and a parameter selection methodology. We use a reduced order model to compute the initial estimates of the spatial and temporal resolutions and an iterative approach that further adjusts the resolution during the simulation without user interaction. The pipeline and the solver are validated based on previously published results, and by comparing the results obtained for 20 cerebral aneurysm cases with those generated by a state-of-the-art commercial solver (Ansys CFX, Canonsburg PA). The automatically selected spatial and temporal resolutions lead to results which closely agree with the state-of-the-art, with an average relative difference of only 2%. Due to the GPU-based parallelization, simulations are computationally efficient, with a median computation time of 40 minutes per simulation.

Computational Hemodynamic Modeling of Arterial Aneurysms: A Mini-Review

Arterial aneurysms are pathological dilations of blood vessels, which can be of clinical concern due to thrombosis, dissection, or rupture. Aneurysms can form throughout the arterial system, including intracranial, thoracic, abdominal, visceral, peripheral, or coronary arteries. Currently, aneurysm diameter and expansion rates are the most commonly used metrics to assess rupture risk. Surgical or endovascular interventions are clinical treatment options, but are invasive and associated with risk for the patient. For aneurysms in locations where thrombosis is the primary concern, diameter is also used to determine the level of therapeutic anticoagulation, a treatment that increases the possibility of internal bleeding. Since simple diameter is often insufficient to reliably determine rupture and thrombosis risk, computational hemodynamic simulations are being developed to help assess when an intervention is warranted. Created from subject-specific data, computational models have the potential to be used to predict growth, dissection, rupture, and thrombus-formation risk based on hemodynamic parameters, including wall shear stress, oscillatory shear index, residence time, and anomalous blood flow patterns. Generally, endothelial damage and flow stagnation within aneurysms can lead to coagulation, inflammation, and the release of proteases, which alter extracellular matrix composition, increasing risk of rupture. In this review, we highlight recent work that investigates aneurysm geometry, model parameter assumptions, and other specific considerations that influence computational aneurysm simulations. By highlighting modeling validation and verification approaches, we hope to inspire future computational efforts aimed at improving our understanding of aneurysm pathology and treatment risk stratification.

A framework for automated and objective modification of tubular structures: Application to the internal carotid artery

Patient-specific medical image-based computational fluid dynamics has been widely used to reveal fundamental insight into mechanisms of cardiovascular disease, for instance, correlating morphology to adverse vascular remodeling. However, segmentation of medical images is laborious, error-prone, and a bottleneck in the development of large databases that are needed to capture the natural variability in morphology. Instead, idealized models, where morphological features are parameterized, have been used to investigate the correlation with flow features, but at the cost of limited understanding of the complexity of cardiovascular flows. To combine the advantages of both approaches, we developed a tool that preserves the patient-specificness inherent in medical images while allowing for parametric alteration of the morphology. In our open-source framework morphMan we convert the segmented surface to a Voronoi diagram, modify the diagram to change the morphological features of interest, and then convert back to a new surface. In this paper, we present algorithms for modifying bifurcation angles, location of branches, cross-sectional area, vessel curvature, shape of bends, and surface roughness. We show qualitative and quantitative validation of the algorithms, performing with an accuracy exceeding 97% in general, and proof-of-concept on combining the tool with computational fluid dynamics. By combining morphMan with appropriate clinical measurements, one could explore the morphological parameter space and resulting hemodynamic response using only a handful of segmented surfaces, effectively minimizing the main bottleneck in image-based computational fluid dynamics.

Hemodynamics of Cerebral Aneurysms: Connecting Medical Imaging and Biomechanical Analysis

In the last two decades, numerous studies have conducted patient-specific computations of blood flow dynamics in cerebral aneurysms and reported correlations between various hemodynamic metrics and aneurysmal disease progression or treatment outcomes. Nevertheless, intra-aneurysmal flow analysis has not been adopted in current clinical practice, and hemodynamic factors usually are not considered in clinical decision making. This review presents the state of the art in cerebral aneurysm imaging and image-based modeling, discussing the advantages and limitations of each approach and focusing on the translational value of hemodynamic analysis. Combining imaging and modeling data obtained from different flow modalities can improve the accuracy and fidelity of resulting velocity fields and flow-derived factors that are thought to affect aneurysmal disease progression. It is expected that predictive models utilizing hemodynamic factors in combination with patient medical history and morphological data will outperform current risk scores and treatment guidelines. Possible future directions include novel approaches enabling data assimilation and multimodality analysis of cerebral aneurysm hemodynamics.

4D-CT angiography versus 3D-rotational angiography as the imaging modality for computational fluid dynamics of cerebral aneurysms

Background and purpose

Computational fluid dynamics (CFD) can provide valuable information regarding intracranial hemodynamics. Patient-specific models can be segmented from various imaging modalities, which may influence the geometric output and thus hemodynamic results. This study aims to compare CFD results from aneurysm models segmented from three-dimensional rotational angiography (3D-RA) versus novel four-dimensional CT angiography (4D-CTA).

Methods

Fourteen patients with 16 cerebral aneurysms underwent novel 4D-CTA followed by 3D-RA. Endoluminal geometries were segmented from each modality using an identical workflow, blinded to the other modality, to produce 28 'original' models. Each was then minimally edited a second time to match length of branches, producing 28 additional 'matched' models. CFD simulations were performed using estimated flow rates for 'original' models (representing real-world experience) and patient-specific flow rates from 4D-CTA for 'matched' models (to control for influence of modality alone).

Results

Overall, geometric and hemodynamic results were consistent between models segmented from 3D-RA and 4D-CTA, with correlations improving after matching to control for operator-introduced variability. Despite smaller 4D-CTA parent artery diameters (3.49±0.97 mm vs 3.78±0.92 mm for 3D-RA; p=0.005) and sac volumes (157 (37–750 mm3) vs 173 (53–770 mm3) for 3D-RA; p=0.0002), sac averages of time-averaged wall shear stress (TAWSS), oscillatory shear (OSI), and high frequency fluctuations (measured by spectral power index, SPI) were well correlated between 3D-RA and 4D-CTA 'matched' control models (TAWSS, R2=0.91; OSI, R2=0.79; SPI, R2=0.90).

Conclusions

Our study shows that CFD performed using 4D-CTA models produces reliable geometric and hemodynamic information in the intracranial circulation. 4D-CTA may be considered as a follow-up imaging tool for hemodynamic assessment of cerebral aneurysms.

Flow-splitting-based computation of outlet boundary conditions for improved cerebrovascular simulation in multiple intracranial aneurysms

PURPOSE

Image-based hemodynamic simulations have great potential for precise blood flow predictions in intracranial aneurysms. Due to model assumptions and simplifications with respect to boundary conditions, clinical acceptance remains limited.

METHODS

Within this study, we analyzed the influence of outflow-splitting approaches on multiple aneurysm studies and present a new outflow-splitting approach that takes the precise morphological vessel cross sections into account. We provide a detailed comparison of five outflow strategies considering eight intracranial aneurysms: zero-pressure configuration (1), a flow splitting inspired by Murray's law with a square (2) and a cubic (3) vessel diameter, a flow splitting incorporating vessel bifurcations based on circular vessel cross sections (4) and our novel flow splitting including vessel bifurcations and anatomical vessel cross sections (5). Other boundary conditions remain constant. For each simulation and each aneurysm, we conducted an evaluation based on common hemodynamic parameters, e.g., normalized wall shear stress and inflow concentration index.

RESULTS

The comparison of five outflow strategies for image-based simulations shows a large variability regarding the parameters of interest. Qualitatively, our strategy based on anatomical cross sections yields a more uniform flow rate distribution with increased aneurysm inflow rates. The commonly used zero-pressure approach shows the largest variations, especially for more distal aneurysms. A rank ordering of multiple aneurysms in one patient might still be possible, since the ordering appeared to be independent of the outflow strategy.

CONCLUSIONS

The results reveal that outlet boundary conditions have a crucial impact on image-based blood flow simulations, especially for multiple aneurysm studies. We could confirm the advantages of the more complex outflow-splitting model (4) including an incremental improvement (5) compared to strategies (1), (2) and (3) for this application scenario. Furthermore, we discourage from using zero-pressure configurations that lack a physiological basis.