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.

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.

Quantifying Smooth Muscles Regional Organization in the Rat Bladder Using Immunohistochemistry, Multiphoton Microscopy and Machine Learning.

The smooth muscle bundles (SMBs) in the bladder act as contractile elements which enable the bladder to void effectively. In contrast to skeletal muscles, these bundles are not highly aligned, rather they are oriented more heterogeneously throughout the bladder wall. In this work, for the first time, this regional orientation of the SMBs is quantified across the whole bladder, without the need for optical clearing or cryosectioning. Immunohistochemistry staining was utilized to visualize smooth muscle cell actin in multiphoton microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature vectors for each pixel were generated using a range of filters, including Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random Forest classifier was subsequently trained to automate the segmentation of SMBs in the MPM images. Finally, the orientation of SMBs in each bladder region was quantified using the CT-FIRE package. This information is essential for biomechanical models of the bladder that include contractile elements.

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.

Three-dimensional wall-thickness distributions of unruptured intracranial aneurysms characterized by micro-computed tomography.

Aneurysmal rupture is associated with wall thinning, but the mechanism is poorly understood. This study aimed to characterize the three-dimensional wall-thickness distributions of unruptured intracranial aneurysms. Five aneurysmal tissues were investigated using micro-computed tomography. First, the wall thickness was related to the aneurysmal wall appearances during surgery. The median wall thicknesses of the translucent and non-translucent walls were 50.56 and 155.93 µm, respectively (p < 0.05) with significant variation in the non-translucent wall thicknesses (p < 0.05). The three-dimensional observations characterized the spatial variation of wall thicknesses. Thin walls showed a uniform thickness profile ranging from 10 to 40 µm, whereas thick walls presented a peaked thickness profile ranging from 300 to 500 µm. In transition walls, the profile undulated due to the formation of focal thin/thick spots. Overall, the aneurysmal wall thicknesses were strongly site-dependent and spatially varied by 10 to 40 times within individual cases. Aneurysmal walls are exposed to wall stress driven by blood pressure. In theory, the magnitude of wall stress is inversely proportional to wall thickness. Thus, the observed spatial variation of wall thickness may increase the spatial variation of wall stress to a similar extent. The irregular wall thickness may yield stress concentration. The observed thin walls and focal thin spots may be caused by excessive wall stresses at the range of mechanical failure inducing wall injuries, such as microscopic tears, during aneurysmal enlargement. The present results suggested that blood pressure (wall stress) may have a potential of acting as a trigger of aneurysmal wall injury.

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.

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.

Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population.

BACKGROUND: Bleb presence in intracranial aneurysms (IAs) is a known indication of instability and vulnerability. OBJECTIVE: To develop and evaluate predictive models of bleb development in IAs based on hemodynamics, geometry, anatomical location, and patient population. METHODS: Cross-sectional data (one time point) of 2395 IAs were used for training bleb formation models using machine learning (random forest, support vector machine, logistic regression, k-nearest neighbor, and bagging). Aneurysm hemodynamics and geometry were characterized using image-based computational fluid dynamics. A separate dataset with 266 aneurysms was used for model evaluation. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), true positive rate (TPR), false positive rate (FPR), precision, and balanced accuracy. RESULTS: The final model retained 18 variables, including hemodynamic, geometrical, location, multiplicity, and morphology parameters, and patient population. Generally, strong and concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns together with larger, more elongated, and more distorted shapes were associated with bleb formation. The best performance on the validation set was achieved by the random forest model (AUC=0.82, TPR=91%, FPR=36%, misclassification error=27%). CONCLUSIONS: Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.

Combining data from multiple sources to study mechanisms of aneurysm disease: Tools and techniques.

INTRODUCTION: Connecting local hemodynamics, biomechanics, and tissue properties in cerebral aneurysms is important for understanding the processes of wall degeneration and subsequent aneurysm progression and rupture. This challenging problem requires integration of data from multiple sources. METHODS: This paper describes the tools and techniques developed to integrate data from multiple sources, including clinical information, 3D imaging, intraoperative videos, ex vivo micro-computed tomography (CT), and multiphoton microscopy. Central to this approach is a 3D tissue model constructed from micro-CT images of aneurysm samples resected during neurosurgery. This model is aligned to vascular models constructed from 3D clinical images and is used to map and compare flow, biomechanics, and tissue data. RESULTS: The approach is illustrated with data of three human intracranial aneurysms. These case studies demonstrated the ability of this approach to study relationships between different factors affecting the aneurysm wall and produced provocative observations that will be further studied with larger series. For instance, "atherosclerotic" and "hyperplastic" looking parts of the aneurysm corresponded to thicker walls and occurred in regions of recirculating flow and low wall shear stress (WSS); thin regions were associated with inflow jets, flow impingement, and high WSS; blebs had walls of varying structures, including calcified, thin, or hyperplastic walls. CONCLUSIONS: The current approach enables the study of interactions of multiple factors thought to be responsible for the progressive degradation and weakening of the aneurysm wall during its evolution.

Experimental insights into flow impingement in cerebral aneurysm by stereoscopic particle image velocimetry: transition from a laminar regime.

This study experimentally investigated the instability of flow impingement in a cerebral aneurysm, which was speculated to promote the degradation of aneurysmal wall. A patient-specific, full-scale and elastic-wall replica of cerebral artery was fabricated from transparent silicone rubber. The geometry of the aneurysm corresponded to that found at 9 days before rupture. The flow in a replica was analysed by quantitative flow visualization (stereoscopic particle image velocimetry) in a three-dimensional, high-resolution and time-resolved manner. The mid-systolic and late-diastolic flows with a Reynolds number of 450 and 230 were compared. The temporal and spatial variations of near-wall velocity at flow impingement delineated its inherent instability at a low Reynolds number. Wall shear stress (WSS) at that site exhibited a combination of temporal fluctuation and spatial divergence. The frequency range of fluctuation was found to exceed significantly that of the heart rate. The high-frequency-fluctuating WSS appeared only during mid-systole and disappeared during late diastole. These results suggested that the flow impingement induced a transition from a laminar regime. This study demonstrated that the hydrodynamic instability of shear layer could not be neglected even at a low Reynolds number. No assumption was found to justify treating the aneurysmal haemodynamics as a fully viscous laminar flow.