Comapping Cellular Content and Extracellular Matrix with Hemodynamics in Intact Arterial Tissues Using Scanning Immunofluorescent Multiphoton Microscopy.

Deviation of blood flow from an optimal range is known to be associated with the initiation and progression of vascular pathologies. Important open questions remain about how the abnormal flow drives specific wall changes in pathologies such as cerebral aneurysms where the flow is highly heterogeneous and complex. This knowledge gap precludes the clinical use of readily available flow data to predict outcomes and improve treatment of these diseases. As both flow and the pathological wall changes are spatially heterogeneous, a crucial requirement for progress in this area is a methodology for acquiring and comapping local vascular wall biology data with local hemodynamic data. Here, we developed an imaging pipeline to address this pressing need. A protocol that employs scanning multiphoton microscopy was developed to obtain three-dimensional (3D) datasets for smooth muscle actin, collagen, and elastin in intact vascular specimens. A cluster analysis was introduced to objectively categorize the smooth muscle cells (SMC) across the vascular specimen based on SMC actin density. Finally, direct quantitative comparison of local flow and wall biology in 3D intact specimens was achieved by comapping both heterogeneous SMC data and wall thickness to patient-specific hemodynamic results.

Asymptomatic carotid artery stenosis is associated with cerebral hypoperfusion.

OBJECTIVE: We have shown that almost 50% of patients with asymptomatic carotid stenosis (ACS) will demonstrate cognitive impairment. Recent evidence has suggested that cerebral hypoperfusion is an important cause of cognitive impairment. Carotid stenosis can restrict blood flow to the brain, with consequent cerebral hypoperfusion. In contrast, cross-hemispheric collateral compensation through the Circle of Willis, and cerebrovascular vasodilation can also mitigate the effects of flow restriction. It is, therefore, critical to develop a clinically relevant measure of net brain perfusion in patients with ACS that could help in risk stratification and in determining the appropriate treatment. To determine whether ACS results in cerebral hypoperfusion, we developed a novel approach to quantify interhemispheric cerebral perfusion differences, measured as the time to peak (TTP) and mean transit time (MTT) delays using perfusion-weighted magnetic resonance imaging (PWI) of the whole brain. To evaluate the utility of using clinical duplex ultrasonography (DUS) to infer brain perfusion, we also assessed the relationship between the PWI findings and ultrasound-based peak systolic velocity (PSV). METHODS: Structural and PWI of the brain and magnetic resonance angiography of the carotid arteries were performed in 20 patients with ≥70% ACS. DUS provided the PSV, and magnetic resonance angiography provided plaque geometric measures at the stenosis. Volumetric perfusion maps of the entire brain from PWI were analyzed to obtain the mean interhemispheric differences for the TTP and MTT delays. In addition, the proportion of brain volume that demonstrated a delay in TTP and MTT was also measured. These proportions were measured for increasing severity of perfusion delays (0.5, 1.0, and 2.0 seconds). Finally, perfusion asymmetries on PWI were correlated with the PSV and stenosis features on DUS using Pearson's correlation coefficients. RESULTS: Of the 20 patients, 18 had unilateral stenosis (8 right and 10 left) and 2 had bilateral stenoses. The interhemispheric (left-right) TTP delays measured for the whole brain volume identified impaired perfusion in the hemisphere ipsilateral to the stenosis in 16 of the 18 patients. More than 45% of the patients had had ischemia in at least one half of their brain volume, with a TTP delay >0.5 second. The TTP and MTT delays showed strong correlations with PSV. In contrast, the correlations with the percentage of stenosis were weaker. The correlations for the PSV were strongest with the perfusion deficits (TTP and MTT delays) measured for the whole brain using our proposed algorithm (r = 0.80 and r = 0.74, respectively) rather than when measured on a single magnetic resonance angiography slice as performed in current clinical protocols (r = 0.31 and r = 0.58, respectively). CONCLUSIONS: Interhemispheric TTP and MTT delay measured for the whole brain using PWI has provided a new tool for assessing cerebral perfusion deficits in patients with ACS. Carotid stenosis was associated with a detectable reduction in ipsilateral brain perfusion compared with the opposite hemisphere in >80% of patients. The PSV measured at the carotid stenosis using ultrasonography correlated with TTP and MTT delays and might serve as a clinically useful surrogate to brain hypoperfusion in these patients.

Calcification in Human Intracranial Aneurysms Is Highly Prevalent and Displays Both Atherosclerotic and Nonatherosclerotic Types.

OBJECTIVE: Although the clinical and biological importance of calcification is well recognized for the extracerebral vasculature, its role in cerebral vascular disease, particularly, intracranial aneurysms (IAs), remains poorly understood. Extracerebrally, 2 distinct mechanisms drive calcification, a nonatherosclerotic, rapid mineralization in the media and a slower, inflammation driven, atherosclerotic mechanism in the intima. This study aims to determine the prevalence, distribution, and type (atherosclerotic, nonatherosclerotic) of calcification in IAs and assess differences in occurrence between ruptured and unruptured IAs. Approach and Results: Sixty-five 65 IA specimens (48 unruptured, 17 ruptured) were resected perioperatively. Calcification and lipid pools were analyzed nondestructively in intact samples using high resolution (0.35 µm) microcomputed tomography. Calcification is highly prevalent (78%) appearing as micro (<500 µm), meso (500 µm-1 mm), and macro (>1 mm) calcifications. Calcification manifests in IAs as both nonatherosclerotic (calcification distinct from lipid pools) and atherosclerotic (calcification in the presence of lipid pools) with 3 wall types: Type I-only calcification, no lipid pools (20/51, 39%), Type II-calcification and lipid pools, not colocalized (19/51, 37%), Type III-calcification colocalized with lipid pools (12/51, 24%). Ruptured IAs either had no calcifications or had nonatherosclerotic micro- or meso-calcifications (Type I or II), without macro-calcifications. CONCLUSIONS: Calcification in IAs is substantially more prevalent than previously reported and presents as both nonatherosclerotic and atherosclerotic types. Notably, ruptured aneurysms had only nonatherosclerotic calcification, had significantly lower calcification fraction, and did not contain macrocalcifications. Improved understanding of the role of calcification in IA pathology should lead to new therapeutic targets.

Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment.

BACKGROUND: Hemodynamic patterns have been associated with cerebral aneurysm instability. For patient-specific computational fluid dynamics (CFD) simulations, the inflow rates of a patient are typically not known. The aim of this study was to analyze the influence of inter- and intra-patient variations of cerebral blood flow on the computed hemodynamics through CFD simulations and to incorporate these variations into statistical models for aneurysm rupture prediction. METHODS: Image data of 1820 aneurysms were used for patient-specific steady CFD simulations with nine different inflow rates per case, capturing inter- and intra-patient flow variations. Based on the computed flow fields, 17 hemodynamic parameters were calculated and compared for the different flow conditions. Next, statistical models for aneurysm rupture were trained in 1571 of the aneurysms including hemodynamic parameters capturing the flow variations either by defining hemodynamic "response variables" (model A) or repeatedly randomly selecting flow conditions by patients (model B) as well as morphological and patient-specific variables. Both models were evaluated in the remaining 249 cases. RESULTS: All hemodynamic parameters were significantly different for the varying flow conditions (p < 0.001). Both the flow-independent "response" model A and the flow-dependent model B performed well with areas under the receiver operating characteristic curve of 0.8182 and 0.8174 ± 0.0045, respectively. CONCLUSIONS: The influence of inter- and intra-patient flow variations on computed hemodynamics can be taken into account in multivariate aneurysm rupture prediction models achieving a good predictive performance. Such models can be applied to CFD data independent of the specific inflow boundary conditions.

Associations of hemodynamics, morphology, and patient characteristics with aneurysm rupture stratified by aneurysm location.

PURPOSE: The mechanisms of cerebral aneurysm rupture are not fully understood. We analyzed the associations of hemodynamics, morphology, and patient age and gender with aneurysm rupture stratifying by location. METHODS: Using image-based models, 20 hemodynamic and 17 morphological parameters were compared in 1931 ruptured and unruptured aneurysms with univariate logistic regression. Rupture rates were compared between males and females as well as younger and older patients and bifurcation versus sidewall aneurysms for different aneurysm locations. Subsequently, associations between hemodynamics and morphology and patient as well as aneurysm characteristics were analyzed for aneurysms at five locations. RESULTS: Compared to unruptured aneurysms, ruptured aneurysms were characterized by a more irregular shape and were exposed to a more adverse hemodynamic environment described by faster flow, higher wall shear stress, more oscillatory shear, and more unstable and complex flows. These associations with rupture status were consistent for different aneurysm locations. Rupture rates were significantly higher in males at the internal carotid artery (ICA) bifurcation, ophthalmic ICA, and the middle cerebral artery (MCA) bifurcation. At the anterior communicating artery (ACOM) and MCA bifurcation, they were significantly higher for younger patients. Bifurcation aneurysms had significantly larger rupture rates at the MCA and posterior communicating artery (PCOM). In these groups with higher rupture rates, aneurysms were characterized by adverse hemodynamics and more complex shapes. CONCLUSION: Hemodynamic and morphological differences between ruptured and unruptured aneurysms are consistent across locations. Adverse morphology and hemodynamics are related to rupture as well as younger age, male gender, and bifurcation aneurysms.

Flow-induced, inflammation-mediated arterial wall remodeling in the formation and progression of intracranial aneurysms.

OBJECTIVE: Unruptured intracranial aneurysms (UIAs) are relatively common lesions that may cause devastating intracranial hemorrhage, thus producing considerable suffering and anxiety in those affected by the disease or an increased likelihood of developing it. Advances in the knowledge of the pathobiology behind intracranial aneurysm (IA) formation, progression, and rupture have led to preclinical testing of drug therapies that would prevent IA formation or progression. In parallel, novel biologically based diagnostic tools to estimate rupture risk are approaching clinical use. Arterial wall remodeling, triggered by flow and intramural stresses and mediated by inflammation, is relevant to both. METHODS: This review discusses the basis of flow-driven vessel remodeling and translates that knowledge to the observations made on the mechanisms of IA initiation and progression on studies using animal models of induced IA formation, study of human IA tissue samples, and study of patient-derived computational fluid dynamics models. RESULTS: Blood flow conditions leading to high wall shear stress (WSS) activate proinflammatory signaling in endothelial cells that recruits macrophages to the site exposed to high WSS, especially through macrophage chemoattractant protein 1 (MCP1). This macrophage infiltration leads to protease expression, which disrupts the internal elastic lamina and collagen matrix, leading to focal outward bulging of the wall and IA initiation. For the IA to grow, collagen remodeling and smooth muscle cell (SMC) proliferation are essential, because the fact that collagen does not distend much prevents the passive dilation of a focal weakness to a sizable IA. Chronic macrophage infiltration of the IA wall promotes this SMC-mediated growth and is a potential target for drug therapy. Once the IA wall grows, it is subjected to changes in wall tension and flow conditions as a result of the change in geometry and has to remodel accordingly to avoid rupture. Flow affects this remodeling process. CONCLUSIONS: Flow triggers an inflammatory reaction that predisposes the arterial wall to IA initiation and growth and affects the associated remodeling of the UIA wall. This chronic inflammation is a putative target for drug therapy that would stabilize UIAs or prevent UIA formation. Moreover, once this coupling between IA wall remodeling and flow is understood, data from patient-specific flow models can be gathered as part of the diagnostic workup and utilized to improve risk assessment for UIA initiation, progression, and eventual rupture.

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics.

OBJECTIVE: Incidental aneurysms pose a challenge for physicians, who need to weigh the rupture risk against the risks associated with treatment and its complications. A statistical model could potentially support such treatment decisions. A recently developed aneurysm rupture probability model performed well in the US data used for model training and in data from two European cohorts for external validation. Because Japanese and Finnish patients are known to have a higher aneurysm rupture risk, the authors' goals in the present study were to evaluate this model using data from Japanese and Finnish patients and to compare it with new models trained with Finnish and Japanese data. METHODS: Patient and image data on 2129 aneurysms in 1472 patients were used. Of these aneurysm cases, 1631 had been collected mainly from US hospitals, 249 from European (other than Finnish) hospitals, 147 from Japanese hospitals, and 102 from Finnish hospitals. Computational fluid dynamics simulations and shape analyses were conducted to quantitatively characterize each aneurysm's shape and hemodynamics. Next, the previously developed model's discrimination was evaluated using the Finnish and Japanese data in terms of the area under the receiver operating characteristic curve (AUC). Models with and without interaction terms between patient population and aneurysm characteristics were trained and evaluated including data from all four cohorts obtained by repeatedly randomly splitting the data into training and test data. RESULTS: The US model's AUC was reduced to 0.70 and 0.72, respectively, in the Finnish and Japanese data compared to 0.82 and 0.86 in the European and US data. When training the model with Japanese and Finnish data, the average AUC increased only slightly for the Finnish sample (to 0.76 ± 0.16) and Finnish and Japanese cases combined (from 0.74 to 0.75 ± 0.14) and decreased for the Japanese data (to 0.66 ± 0.33). In models including interaction terms, the AUC in the Finnish and Japanese data combined increased significantly to 0.83 ± 0.10. CONCLUSIONS: Developing an aneurysm rupture prediction model that applies to Japanese and Finnish aneurysms requires including data from these two cohorts for model training, as well as interaction terms between patient population and the other variables in the model. When including this information, the performance of such a model with Japanese and Finnish data is close to its performance with US or European data. These results suggest that population-specific differences determine how hemodynamics and shape associate with rupture risk in intracranial aneurysms.

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts.

BACKGROUND: For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions. METHODS: Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model's performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts. RESULTS: When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model's prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14). CONCLUSIONS: The model's performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.

Development of a statistical model for discrimination of rupture status in posterior communicating artery aneurysms.

BACKGROUND: Intracranial aneurysms at the posterior communicating artery (PCOM) are known to have high rupture rates compared to other locations. We developed and internally validated a statistical model discriminating between ruptured and unruptured PCOM aneurysms based on hemodynamic and geometric parameters, angio-architectures, and patient age with the objective of its future use for aneurysm risk assessment. METHODS: A total of 289 PCOM aneurysms in 272 patients modeled with image-based computational fluid dynamics (CFD) were used to construct statistical models using logistic group lasso regression. These models were evaluated with respect to discrimination power and goodness of fit using tenfold nested cross-validation and a split-sample approach to mimic external validation. RESULTS: The final model retained maximum and minimum wall shear stress (WSS), mean parent artery WSS, maximum and minimum oscillatory shear index, shear concentration index, and aneurysm peak flow velocity, along with aneurysm height and width, bulge location, non-sphericity index, mean Gaussian curvature, angio-architecture type, and patient age. The corresponding area under the curve (AUC) was 0.8359. When omitting data from each of the three largest contributing hospitals in turn, and applying the corresponding model on the left-out data, the AUCs were 0.7507, 0.7081, and 0.5842, respectively. CONCLUSIONS: Statistical models based on a combination of patient age, angio-architecture, hemodynamics, and geometric characteristics can discriminate between ruptured and unruptured PCOM aneurysms with an AUC of 84%. It is important to include data from different hospitals to create models of aneurysm rupture that are valid across hospital populations.

Noninvasive characterization of carotid plaque strain.

OBJECTIVE: Current risk stratification of internal carotid artery plaques based on diameter-reducing percentage stenosis may be unreliable because ischemic stroke results from plaque disruption with atheroembolization. Biomechanical forces acting on the plaque may render it vulnerable to rupture. The feasibility of ultrasound-based quantification of plaque displacement and strain induced by hemodynamic forces and their relationship to high-risk plaques have not been determined. We studied the feasibility and reliability of carotid plaque strain measurement from clinical B-mode ultrasound images and the relationship of strain to high-risk plaque morphology. METHODS: We analyzed carotid ultrasound B-mode cine loops obtained in patients with asymptomatic ≥50% stenosis during routine clinical scanning. Optical flow methods were used to quantify plaque motion and shear strain during the cardiac cycle. The magnitude (maximum absolute shear strain rate [MASSR]) and variability (entropy of shear strain rate [ESSR] and variance of shear strain rate [VSSR]) of strain were combined into a composite shear strain index (SSI), which was assessed for interscan repeatability and correlated with plaque echolucency. RESULTS: Nineteen patients (mean age, 70 years) constituting 36 plaques underwent imaging; 37% of patients (n = 7) showed high strain (SSI ≥0.5; MASSR, 2.2; ESSR, 39.7; VSSR, 0.03) in their plaques; the remaining clustered into a low-strain group (SSI <0.5; MASSR, 0.58; ESSR, 21.2; VSSR, 0.002). The area of echolucent morphology was greater in high-strain plaques vs low-strain plaques (28% vs 17%; P = .018). Strain measurements showed low variability on Bland-Altman plots with cluster assignment agreement of 76% on repeated scanning. Two patients developed a stroke during 2 years of follow-up; both demonstrated high SSI (≥0.5) at baseline. CONCLUSIONS: Carotid plaque strain is reliably computed from routine B-mode imaging using clinical ultrasound machines. High plaque strain correlates with known high-risk echolucent morphology. Strain measurement can complement identification of patients at high risk for plaque disruption and stroke.

Mechanisms Involved in the Formation of Biocompatible Lipid Polymeric Hollow Patchy Particles.

Patchy polymeric particles have anisotropic surface domains that can be remarkably useful in diverse medical and industrial fields because of their ability to simultaneously present two different surface chemistries on the same construct. In this article, we report the mechanisms involved in the formation of novel lipid-polymeric hollow patchy particles during their synthesis. By cross-sectioning the patchy particles, we found that a phase segregation phenomenon occurs between the core, shell, and patch. Importantly, we found that the shear stress that the polymer blend undergoes during the particle synthesis is the most important parameter for the formation of these patchy particles. In addition, we found that the interplay of solvent-solvent, polymer-solvent, and polymer-polymer-solvent interactions generates particles with different surface morphologies. Understanding the mechanisms involved in the formation of patchy particles allows us to have a better control on their physicochemical properties. Therefore, these fundamental studies are critical to achieve batch control and scalability, which are essential aspects that must be addressed in any type of particle synthesis to be safely used in medicine.

The Computational Fluid Dynamics Rupture Challenge 2013--Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent "outliers" (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

A one-dimensional computational model for blood flow in an elastic blood vessel with a rigid catheter.

Strokes are one of the leading causes of death in the United States. Stroke treatment involves removal or dissolution of the obstruction (usually a clot) in the blocked artery by catheter insertion. A computer simulation to systematically plan such patient-specific treatments needs a network of about 105 blood vessels including collaterals. The existing computational fluid dynamic (CFD) solvers are not employed for stroke treatment planning as they are incapable of providing solutions for such big arterial trees in a reasonable amount of time. This work presents a novel one-dimensional mathematical formulation for blood flow modeling in an elastic blood vessel with a centrally placed rigid catheter. The governing equations are first-order hyperbolic partial differential equations, and the hypergeometric function needs to be computed to obtain the characteristic system of these hyperbolic equations. We employed the Discontinuous Galerkin method to solve the hyperbolic system and validated the implementation by comparing it against a well-established 3D CFD solver using idealized vessels and a realistic truncated arterial network. The results showed clinically insignificant differences in steady flow cases, with overall variations between 1D and 3D models remaining below 10%. Additionally, the solver accurately captured wave reflection phenomena at domain discontinuities in unsteady cases. A primary advantage of this model over 3D solvers is its ease in obtaining a discretized geometry of complex vasculatures with multiple arterial branches. Thus, the 1D computational model offers good accuracy and applicability in simulating complex vasculatures, demonstrating promising potential for investigating patient-specific endovascular interventions in strokes.

Evaluation of predictive models of aneurysm focal growth and bleb development using machine learning techniques.

BACKGROUND: The presence of blebs increases the rupture risk of intracranial aneurysms (IAs). OBJECTIVE: To evaluate whether cross-sectional bleb formation models can identify aneurysms with focalized enlargement in longitudinal series. METHODS: Hemodynamic, geometric, and anatomical variables derived from computational fluid dynamics models of 2265 IAs from a cross-sectional dataset were used to train machine learning (ML) models for bleb development. ML algorithms, including logistic regression, random forest, bagging method, support vector machine, and K-nearest neighbors, were validated using an independent cross-sectional dataset of 266 IAs. The models' ability to identify aneurysms with focalized enlargement was evaluated using a separate longitudinal dataset of 174 IAs. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), the sensitivity and specificity, positive predictive value, negative predictive value, F1 score, balanced accuracy, and misclassification error. RESULTS: The final model, with three hemodynamic and four geometrical variables, along with aneurysm location and morphology, identified strong inflow jets, non-uniform wall shear stress with high peaks, larger sizes, and elongated shapes as indicators of a higher risk of focal growth over time. The logistic regression model demonstrated the best performance on the longitudinal series, achieving an AUC of 0.9, sensitivity of 85%, specificity of 75%, balanced accuracy of 80%, and a misclassification error of 21%. CONCLUSIONS: Models trained with cross-sectional data can identify aneurysms prone to future focalized growth with good accuracy. These models could potentially be used as early indicators of future risk in clinical practice.

Phenotyping calcification in vascular tissues using artificial intelligence.

Vascular calcification is implicated as an important factor in major adverse cardiovascular events (MACE), including heart attack and stroke. A controversy remains over how to integrate the diverse forms of vascular calcification into clinical risk assessment tools. Even the commonly used calcium score for coronary arteries, which assumes risk scales positively with total calcification, has important inconsistencies. Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes. However, studies of these kinds are hindered by the lack of high-throughput, objective, and non-destructive tools for classifying calcification in imaging data sets. Here, we introduce a new classification system for phenotyping calcification along with a semi-automated, non-destructive pipeline that can distinguish these phenotypes in even atherosclerotic tissues. The pipeline includes a deep-learning-based framework for segmenting lipid pools in noisy µ-CT images and an unsupervised clustering framework for categorizing calcification based on size, clustering, and topology. This approach is illustrated for five vascular specimens, providing phenotyping for thousands of calcification particles across as many as 3200 images in less than seven hours. Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for tissue and lipid pool, respectively, with training and validation needed on only 13 images despite the high heterogeneity in these tissues. By introducing an efficient and comprehensive approach to phenotyping calcification, this work enables large-scale studies to identify a more reliable indicator of the risk of cardiovascular events, a leading cause of global mortality and morbidity.

Co-mapping Cellular Content and Extracellular Matrix with Hemodynamics in Intact Arterial Tissues Using Scanning Immunofluorescent Multiphoton Microscopy.

Deviation of blood flow from an optimal range is known to be associated with the initiation and progression of vascular pathologies. Important open questions remain about how the abnormal flow drives specific wall changes in pathologies such as cerebral aneurysms where the flow is highly heterogeneous and complex. This knowledge gap precludes the clinical use of readily available flow data to predict outcomes and improve treatment of these diseases. As both flow and the pathological wall changes are spatially heterogeneous, a crucial requirement for progress in this area is a methodology for co-mapping local data from vascular wall biology with local hemodynamic data. In this study, we developed an imaging pipeline to address this pressing need. A protocol that employs scanning multiphoton microscopy was designed to obtain 3D data sets for smooth muscle actin, collagen and elastin in intact vascular specimens. A cluster analysis was developed to objectively categorize the smooth muscle cells (SMC) across the vascular specimen based on SMC density. In the final step in this pipeline, the location specific categorization of SMC, along with wall thickness was co-mapped with patient specific hemodynamic results, enabling direct quantitative comparison of local flow and wall biology in 3D intact specimens.

In vitro evaluation of flow diverter performance using a human fibrinogen-based flow model.

OBJECTIVE: Fibrin deposition represents a key step in aneurysm occlusion, promoting endothelization of implants and connective tissue organization as part of the aneurysm-healing mechanism. In this study, the authors introduce a novel in vitro testing platform for flow diverters based on human fibrinogen. METHODS: A flow diverter was deployed in 4 different glass models. The glass models had the same internal parent artery (4 mm) and aneurysm (8 mm) diameters with varying parent artery angulations (paraophthalmic, sidewall, bifurcation, and slightly curved models). The neck size and area were 4 mm and 25 mm2, respectively. Human fibrinogen (330 mg/dl) was circulated within the glass models at varying flow rates (0, 3, 4, and 5 ml/sec) with or without heparin, calcium chloride, and thrombin for as long as 6 hours or until complete fibrin coverage of the flow diverter's neck was achieved. Aneurysm neck coverage was defined as macroscopic fibrin deposition occluding the flow diverters' pores. Flow characteristics after flow diverter deployment were assessed with computational fluid dynamics analysis. The effects of flow rates, heparin, calcium chloride, and thrombin on fibrin deposition rates were tested using 1-way ANOVA and the Tukey test. RESULTS: A total of 84 replicates were performed. Human fibrin did not accumulate on the flow diverter stents under static conditions. The fibrin deposition rate on the aneurysm neck was significantly greater with the 5 ml/sec flow rate as compared to 3 ml/sec for all models. The paraophthalmic model had the highest inflow velocity of 48.7 cm/sec. The bifurcation model had the highest maximum shear stress (SS) and maximum normalized shear stress values at the device cells at 843.3 dyne/cm2 and 35.1 SS/SSinflow, respectively. The fibrin deposition rates of the paraophthalmic and bifurcation models were significantly higher than those of sidewall and slightly curved models for all additive or flow rate comparisons (p = 0.001 for all comparisons). The incorporation of thrombin significantly increased the fibrin deposition rates across all models (p = 0.001 for all models). CONCLUSIONS: Rates of fibrin deposition varied widely across different configurations and additive conditions in this novel in vitro model system. Fibrin accumulation started at the aneurysm inflow zone where flow velocity and shear stress were the highest. The primary factors influencing fibrin deposition included flow velocities, shear stress, and the addition of thrombin at a physiological concentration. Further research is needed to test the clinical utility of fibrinogen-based models for patient-specific aneurysms.

Distribution of rupture sites and blebs on intracranial aneurysm walls suggests distinct rupture patterns in ACom and MCA aneurysms.

The mechanisms behind intracranial aneurysm formation and rupture are not fully understood, with factors such as location, patient demographics, and hemodynamics playing a role. Additionally, the significance of anatomical features like blebs in ruptures is debated. This highlights the necessity for comprehensive research that combines patient-specific risk factors with a detailed analysis of local hemodynamic characteristics at bleb and rupture sites. Our study analyzed 359 intracranial aneurysms from 268 patients, reconstructing patient-specific models for hemodynamic simulations based on 3D rotational angiographic images and intraoperative videos. We identified aneurysm subregions and delineated rupture sites, characterizing blebs and their regional overlap, employing statistical comparisons across demographics, and other risk factors. This work identifies patterns in aneurysm rupture sites, predominantly at the dome, with variations across patient demographics. Hypertensive and anterior communicating artery (ACom) aneurysms showed specific rupture patterns and bleb associations, indicating two pathways: high-flow in ACom with thin blebs at impingement sites and low-flow, oscillatory conditions in middle cerebral artery (MCA) aneurysms fostering thick blebs. Bleb characteristics varied with gender, age, and smoking, linking rupture risks to hemodynamic factors and patient profiles. These insights enhance understanding of the hemodynamic mechanisms leading to rupture events. This analysis elucidates the role of localized hemodynamics in intracranial aneurysm rupture, challenging the emphasis on location by revealing how flow variations influence stability and risk. We identify two pathways to wall failure-high-flow and low-flow conditions-highlighting the complexity of aneurysm behavior. Additionally, this research advances our knowledge of how inherent patient-specific characteristics impact these processes, which need further investigation.

Description of the local hemodynamic environment in intracranial aneurysm wall subdivisions.

Intracranial aneurysms (IAs) pose severe health risks influenced by hemodynamics. This study focuses on the intricate characterization of hemodynamic conditions within the IA walls and their influence on bleb development, aiming to enhance understanding of aneurysm stability and the risk of rupture. The methods emphasized utilizing a comprehensive dataset of 359 IAs and 213 IA blebs from 268 patients to reconstruct patient-specific vascular models, analyzing blood flow using finite element methods to solve the unsteady Navier-Stokes equations, the segmentation of aneurysm wall subregions and the hemodynamic metrics wall shear stress (WSS), its metrics, and the critical points in WSS fields were computed and analyzed across different aneurysm subregions defined by saccular, streamwise, and topographical divisions. The results revealed significant variations in these metrics, correlating distinct hemodynamic environments with wall features on the aneurysm walls, such as bleb formation. Critical findings indicated that regions with low WSS and high OSI, particularly in the body and central regions of aneurysms, are prone to conditions that promote bleb formation. Conversely, areas exposed to high WSS and positive divergence, like the aneurysm neck, inflow, and outflow regions, exhibited a different but substantial risk profile for bleb development, influenced by flow impingements and convergences. These insights highlight the complexity of aneurysm behavior, suggesting that both high and low-shear environments can contribute to aneurysm pathology through distinct mechanisms.

Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography.

Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3T time-of-flight MRA high-resolution (620 µm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age=31.2 ± 10.7, range=19-64 years). We present in-depth morphometric analyses of the global and local anatomical features of these arbors. The overall structure and size of the vasculature did not significantly differ across genders, ages, or hemispheres. The total length of the three major arterial trees stemming from the circle of Willis (from smallest to largest: the posterior, anterior, and middle cerebral arteries; or PCAs, ACAs, and MCAs, respectively) followed an approximate 1:2:4 proportion. Arterial size co-varied across individuals: subjects with one artery longer than average tended to have all other arteries also longer than average. There was no net right-left difference across the population in any of the individual arteries, but ACAs were more lateralized than MCAs. MCAs, ACAs, and PCAs had similar branch-level properties such as bifurcation angles. Throughout the arterial vasculature, there were considerable differences between branch types: bifurcating branches were significantly shorter and straighter than terminating branches. Furthermore, the length and meandering of bifurcating branches increased with age and with path distance from the circle of Willis. All reconstructions are freely distributed through a public database to enable additional analyses and modeling (cng.gmu.edu/brava).