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

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

Thrombosis is a condition closely related to cerebral aneurysms and controlled thrombosis is the main purpose of endovascular embolization treatment. The mechanisms governing thrombus initiation and evolution in cerebral aneurysms have not been fully elucidated and this presents challenges for interventional planning. Significant effort has been directed towards developing computational methods aimed at streamlining the interventional planning process for unruptured cerebral aneurysm treatment. Included in these methods are computational models of thrombus development following endovascular device placement. The main challenge with developing computational models for thrombosis in disease cases is that there exists a wide body of literature that addresses various aspects of the clotting process, but it may not be obvious what information is of direct consequence for what modeling purpose (e.g., for understanding the effect of endovascular therapies). The aim of this review is to present the information so it will be of benefit to the community attempting to model cerebral aneurysm thrombosis for interventional planning purposes, in a simplified yet appropriate manner. The paper begins by explaining current understanding of physiological coagulation and highlights the documented distinctions between the physiological process and cerebral aneurysm thrombosis. Clinical observations of thrombosis following endovascular device placement are then presented. This is followed by a section detailing the demands placed on computational models developed for interventional planning. Finally, existing computational models of thrombosis are presented. This last section begins with description and discussion of physiological computational clotting models, as they are of immense value in understanding how to construct a general computational model of clotting. This is then followed by a review of computational models of clotting in cerebral aneurysms, specifically. Even though some progress has been made towards computational predictions of thrombosis following device placement in cerebral aneurysms, many gaps still remain. Answering the key questions will require the combined efforts of the clinical, experimental and computational communities. [ABSTRACT FROM AUTHOR]

Background: Using computational fluid dynamics (CFD) to compute the hemodynamics in cerebral aneurysms has received much attention in the last decade. The usability of these methods depends on the quality of the computations, highlighted in recent discussions. The purpose of this study is to investigate the convergence of common hemodynamic indicators with respect to numerical resolution. Methods: 38 middle cerebral artery bifurcation aneurysms were studied at two different resolutions (one comparable to most studies, and one finer). Relevant hemodynamic indicators were collected from two of the most cited studies, and were compared at the two refinements. In addition, correlation to rupture was investigated. Results: Most of the hemodynamic indicators were very well resolved at the coarser resolutions, correlating with the finest resolution with a correlation coefficient >0.95. The oscillatory shear index (OSI) had the lowest correlation coefficient of 0.83. A logarithmic Bland-Altman plot revealed noticeable variations in the proportion of the aneurysm under low shear, as well as in spatial and temporal gradients not captured by the correlation alone. Conclusion: Statistically, hemodynamic indicators agree well across the different resolutions studied here. However, there are clear outliers visible in several of the hemodynamic indicators, which suggests that special care should be taken when considering individual assessment. [ABSTRACT FROM AUTHOR]

A new automatic approach for saccular intracranial aneurysm isolation is proposed in this work. Due to the inter- and intra-observer variability in manual delineation of the aneurysm neck, a definition based on a minimum cost path around the aneurysm sac is proposed that copes with this variability and is able to make consistent measurements along different data sets, as well as to automate and speedup the analysis of cerebral aneurysms. The method is based on the computation of a minimal path along a scalar field obtained on the vessel surface, to find the aneurysm neck in a robust and fast manner. The computation of the scalar field on the surface is obtained using a fast marching approach with a speed function based on the exponential of the distance from the centerline bifurcation between the aneurysm dome and the parent vessels. In order to assure a correct topology of the aneurysm sac, the neck computation is constrained to a region defined by a surface Voronoi diagram obtained from the branches of the vessel centerline. We validate this method comparing our results in 26 real cases with manual aneurysm isolation obtained using a cut-plane, and also with results obtained using manual delineations from three different observers by comparing typical morphological measures. [ABSTRACT FROM AUTHOR]

Purpose: Morphological descriptors are practical and essential biomarkers for diagnosis and treatment selection for intracranial aneurysm management according to the current guidelines in use. Nevertheless, relatively little work has been dedicated to improve the three-dimensional quantification of aneurysmal morphology, to automate the analysis, and hence to reduce the inherent intra and interobserver variability of manual analysis. In this paper we propose a methodology for the automated isolation and morphological quantification of saccular intracranial aneurysms based on a 3D representation of the vascular anatomy.Method: This methodology is based on the analysis of the vasculature skeleton's topology and the subsequent application of concepts from deformable cylinders. These are expanded inside the parent vessel to identify different regions and discriminate the aneurysm sac from the parent vessel wall. The method renders as output the surface representation of the isolated aneurysm sac, which can then be quantified automatically. The proposed method provides the means for identifying the aneurysm neck in a deterministic way. The results obtained by the method were assessed in two ways: they were compared to manual measurements obtained by three independent clinicians as normally done during diagnosis and to automated measurements from manually isolated aneurysms by three independent operators, nonclinicians, experts in vascular image analysis. All the measurements were obtained using in-house tools. The results were qualitatively and quantitatively compared for a set of the saccular intracranial aneurysms (n = 26).Results: Measurements performed on a synthetic phantom showed that the automated measurements obtained from manually isolated aneurysms where the most accurate. The differences between the measurements obtained by the clinicians and the manually isolated sacs were statistically significant (neck width: p < 0.001, sac height: p = 0.002). When comparing clinicians' measurements to automatically isolated sacs, only the differences for the neck width were significant (neck width: p < 0.001, sac height: p = 0.95). However, the correlation and agreement between the measurements obtained from manually and automatically isolated aneurysms for the neck width: p = 0.43 and sac height: p = 0.95 where found.Conclusions: The proposed method allows the automated isolation of intracranial aneurysms, eliminating the interobserver variability. In average, the computational cost of the automated method (2 min 36 s) was similar to the time required by a manual operator (measurement by clinicians: 2 min 51 s, manual isolation: 2 min 21 s) but eliminating human interaction. The automated measurements are irrespective of the viewing angle, eliminating any bias or difference between the observer criteria. Finally, the qualitative assessment of the results showed acceptable agreement between manually and automatically isolated aneurysms. [ABSTRACT FROM AUTHOR]

Purpose: In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior. Methods: A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations. Results: Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm. Conclusions: Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results. [ABSTRACT FROM AUTHOR]

This paper presents a technique to estimate and model patient-specific pulsatility of cerebral aneurysms over one cardiac cycle, using 3D rotational X-ray angiography (3DRA) acquisitions. Aneurysm pulsation is modeled as a time varying B-spline tensor field representing the deformation applied to a reference volume image, thus producing the instantaneous morphology at each time point in the cardiac cycle. The estimated deformation is obtained by matching multiple simulated projections of the deforming volume to their corresponding original projections. A weighting scheme is introduced to account for the relevance of each original projection for the selected time point. The wide coverage of the projections, together with the weighting scheme, ensures motion consistency in all directions. The technique has been tested on digital and physical phantoms that are realistic and clinically relevant in terms of geometry, pulsation and imaging conditions. Results from digital phantom experiments demonstrate that the proposed technique is able to recover subvoxel pulsation with an error lower than 10% of the maximum pulsation in most cases. The experiments with the physical phantom allowed demonstrating the feasibility of pulsation estimation as well as identifying different pulsation regions under clinical conditions. [ABSTRACT FROM AUTHOR]

Abstract: Segmentation of vascular structures is a difficult and challenging task. In this article, we present an algorithm devised for the segmentation of such structures. Our technique consists in a geometric deformable model with associated energy functional that incorporates high-order multiscale features in a non-parametric statistical framework. Although the proposed segmentation method is generic, it has been applied to the segmentation of cerebral aneurysms in 3DRA and CTA. An evaluation study over 10 clinical datasets indicate that the segmentations obtained by our method present a high overlap index with respect to the ground-truth (91.13% and 73.31%, respectively) and that the mean error distance from the surface to the ground truth is close to the in-plane resolution (0.40 and 0.38mm, respectively). Besides, our technique favorably compares to other alternative techniques based on deformable models, namely parametric geodesic active regions and active contours without edges. [Copyright &y& Elsevier]

Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high‐fidelity computational models and potentially achieve large gains in execution speed. Machine learning (ML) using neural networks has been used to overcome limitations of traditional ROM techniques when applied to nonlinear problems, which has led to the recent development of reduced order models augmented by machine learning (ML‐ROMs). However, the performance of ML‐ROMs is yet to be widely evaluated in realistic applications and questions remain regarding the optimal design of ML‐ROMs. In this study, we investigate the application of a non‐intrusive parametric ML‐ROM to a nonlinear, time‐dependent fluid dynamics problem in a complex 3D geometry. We construct the ML‐ROM using POD for dimensionality reduction and neural networks for interpolation of the ROM coefficients. We compare three different network designs in terms of approximation accuracy and performance. We test our ML‐ROM on a flow problem in intracranial aneurysms, where flow variability effects are important when evaluating rupture risk and simulating treatment outcomes. The best‐performing network design in our comparison used a two‐stage POD reduction, a technique rarely used in previous studies. The best‐performing ROM achieved mean test accuracies of 98.6% and 97.6% in the parent vessel and the aneurysm, respectively, while providing speed‐up factors of the order 105$$ {10}^5 $$. [ABSTRACT FROM AUTHOR]

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

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

• Proposed a 3D patch-based multi-class model for vessel and aneurysm segmentation on 3DRA images. • Proposed cascaded transformer block, multi-view block, learnable downsample block and wide block to help address class imbalance problem. • Designed a multi-class network with weighted Dice loss and set aneurysms as a subclass of vessels to help address inter-class interference. • Designed a new post-processing pipeline including majority voting and self-refinement to make up for the deficiency of patch-based learning then help address inter-institutional data variability. • Robust performance on clinical data from four different sources. The proposed method is superior compared to popular segmentation methods. Background and Objectives: Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen. Methods: We propose a novel multi-class convolutional neural network to tackle these challenges and facilitate the automatic segmentation of cerebral vessels and aneurysms in 3DRA images. The proposed model is trained and evaluated on an internal multi-center dataset and an external publicly available challenge dataset. Results: On the internal clinical dataset, our method consistently outperformed several state-of-the-art approaches for vessel and aneurysm segmentation, achieving an average Dice score of 0.81 (0.15 higher than nnUNet) and an average surface-to-surface error of 0.20 mm (less than the in-plane resolution (0.35 mm/pixel)) for aneurysm segmentation; and an average Dice score of 0.91 and average surface-to-surface error of 0.25 mm for vessel segmentation. In 223 cases of a clinical dataset, our method accurately segmented 190 aneurysm cases. Conclusions: The proposed approach can help address class imbalance problems and inter-class interference problems in multi-class segmentation. Besides, this method performs consistently on clinical datasets from four different sources and the generated results are qualified for hemodynamic simulation. Code available at https://github.com/cistib/vessel-aneurysm-segmentation. [ABSTRACT FROM AUTHOR]

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

Endovascular coiling aims to isolate the aneurysm from blood circulation by altering hemodynamics inside the aneurysm and triggering blood coagulation. Computational fluid dynamics (CFD) techniques have the potential to predict the post-operative hemodynamics and to investigate the complex interaction between blood flow and coils. The purpose of this work is to study the influence of blood viscosity on hemodynamics in coiled aneurysms. Three image-based aneurysm models were used. Each case was virtually coiled with a packing density of around 30%. CFD simulations were performed in coiled and untreated aneurysm geometries using a Newtonian and a Non-Newtonian fluid models. Newtonian fluid slightly overestimates the intra-aneurysmal velocity inside the aneurysm before and after coiling. There were numerical differences between fluid models on velocity magnitudes in coiled simulations. Moreover, the non-Newtonian fluid model produces high viscosity ( > 0.007 [Pa s]) at aneurysm fundus after coiling. Nonetheless, these local differences and high-viscous regions were not sufficient to alter the main flow patterns and velocity magnitudes before and after coiling. To evaluate the influence of coiling on intra-aneurysmal hemodynamics, the assumption of a Newtonian fluid can be used. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Minimally invasive treatment approaches, like the implantation of percutaneous stents, are becoming more popular every day for the treatment of intracranial aneurysms. The outcome of such treatments is related to factors like vessel and aneurysm geometry, hemodynamic conditions and device design. For this reason, having a tool for assessing stenting alternatives beforehand is crucial. Methodology: The Fast Virtual Stenting (FVS) method, which provides an estimation of the configuration of intracranial stents when released in realistic geometries, is proposed in this paper. This method is based on constrained simplex deformable models. The constraints are used to account for the stent design. An algorithm for its computational implementation is also proposed. The performance of the proposed methodology was contrasted with real stents released in a silicone phantom. Results: In vitro experiments were performed on the phantom where a contrast injection was performed. Subsequently, corresponding Computational Fluid Dynamics (CFD) analyzes were carried out on a digital replica of the phantom with the virtually released stent. Virtual angiographies are used to compare in vitro experiments and CFD analysis. Contrast time–density curves for in vitro and CFD data were generated and used to compare them. Conclusions: Results of both experiments resemble very well, especially when comparing the contrast density curves. The use of FVS methodology in the clinical environment could provide additional information to clinicians before the treatment to choose the therapy that best fits the patient. [Copyright &y& Elsevier]

Abstract: Objective: The mechanisms by which smoking and hypertension lead to increased incidence of intracranial aneurysm (IA) formation remain poorly understood. The current study investigates the effects of these risk factors on wall shear stress (WSS) and oscillatory shear index (OSI) at the site of IA initiation. Methods: Two (n =2) IAs from two patients with history of smoking and hypertension were artificially removed with the help of software @neuFuse (Supercomputing Solutions®, Bologna, Italy) and the vessel geometry reconstructed to mimic the condition prior to IA formation. Two computational fluid dynamics (CFD) analyses were performed on each data-set by using in turn the normal physiological values of blood viscosity (BV), and high BV values specific to smoking and hypertension, obtained from literature. Results: At normal BV, high WSS (>15Pa) was observed at the site of IA initiation in both patients. When BV values specific to smoking and hypertension were used, both the areas affected by high WSS (>15Pa) and the maximum WSS were increased whilst the magnitude and distribution of OSI showed no significant change. Conclusions: Long-term exposure to high WSS may result in an increased risk of IA development. An incremental increase in areas of high WSS observed secondary to smoking and hypertension may indicate a further increase in the risk of IA initiation. Interestingly, the relationship between BV and the area of increased WSS was not linear, reflecting the need for patient-specific CFD analysis. [Copyright &y& Elsevier]

• A fine blood vessel segmentation approach designed for 3D renal artery. • Tensor-based graph-cut method using Riemannian matric. • High segmentation accuracy on both simulated and clinical dataset. Blood vessel segmentation plays a fundamental role in many computer-aided diagnosis (CAD) systems, such as coronary artery stenosis quantification, cerebral aneurysm quantification, and retinal vascular tree analysis. Fine blood vessel segmentation can help build a more accurate computer-aided diagnosis system and help physicians gain a better understanding of vascular structures. The purpose of this article is to develop a blood vessel segmentation method that can improve segmentation accuracy in tiny blood vessels. In this work, we propose a tensor-based graph-cut method for blood vessel segmentation. With our method, each voxel can be modeled by a second-order tensor, allowing the capture of the intensity information and the geometric information for building a more accurate model for blood vessel segmentation. We compared our proposed method's accuracy to several state-of-the-art blood vessel segmentation algorithms and performed experiments on both simulated and clinical CT datasets. Both experiments showed that our method achieved better state-of-the-art results than the competing techniques. The mean centerline overlap ratio of our proposed method is 84% on clinical CT data. Our proposed blood vessel segmentation method outperformed other state-of-the-art methods by 10% on clinical CT data. Tiny blood vessels in clinical CT data with a 1-mm radius can be extracted using the proposed technique. The experiments on a clinical dataset showed that the proposed method significantly improved the segmentation accuracy in tiny blood vessels. [ABSTRACT FROM AUTHOR]