A Highly Automated Computational Method for Modeling of Intracranial Aneurysm Hemodynamics

Different Hemodynamic Characteristics and Resulting in Different Risks of Rupture Between Wide-Neck and Narrow-Neck Aneurysms

Objective

This study aimed to determine the ruptured rate and hemodynamic difference between wide-neck aneurysms (WNAs) and narrow-neck aneurysms (NNAs), as well as the hemodynamic parameters of risk factors for aneurysm rupture.

Methods

A total of 121 cases of intracranial aneurysms (IAs) were studied retrospectively between January 2019 and April 2021 at Renmin Hospital of Wuhan University. Intracranial aneurysms were classified into four types: ruptured wide-neck aneurysms (RWNAs), unruptured wide-neck aneurysms (UWNAs), ruptured narrow-neck aneurysms (RNNAs), and unruptured narrow-neck aneurysms (UNNAs). The Chi-square test was used to compare differences in rupture ratios. The clinical characteristics and hemodynamics were analyzed statistically to reveal the rupture risk factors. Moreover, significant parameters were subjected to binary logistic regression analysis to identify the independent predictive factors. The receiver operating characteristic (ROC) curve was performed to obtain cutoff values.

Results

WNAs ruptured more frequently than NNAs (P = 0.033). Ruptured intracranial aneurysms (RIAs) were characterized by significantly higher intra-aneurysmal pressure (IAP), wall shear stress (WSS), wall shear stress gradient (WSSG), and lower normalized wall shear stress (NWSS) than unruptured intracranial aneurysms (UIAs). RWNAs had higher IAP, WSS, and lower NWSS than UWNAs (P < 0.05). RNNAs had higher IAP, Streamwise WSSG and lower NWSS compared to UNNAs (P < 0.05). Binary logistic regression revealed that IAP and WSS were independent predictive risk factors for WNAs rupture, with cut-off values of 405.5 and 6.66 Pa, respectively. Also, IAP was an independent predictive risk factor for NNA rupture, with a cut-off value of 255.3 Pa.

Conclusions

Wide-neck aneurysms and narrow-neck aneurysms have diverse hemodynamics, which prompts a higher rupture ratio for WNAs. IAP could characterize the rupture risk in both WNAs and NNAs independently, but WSS could only predict the rupture risk in WNAs. This research might assist neurosurgeons with fostering a more sensible strategy for the treatment of IAs.

Artificial Intelligence Applications in Intracranial Aneurysm: Achievements, Challenges and Opportunities

Intracranial aneurysms present in about 3% of the general population and the number of detected aneurysms is continuously rising with the advances in imaging techniques. Intracranial aneurysm rupture carries a high risk of death or permanent disabilities; therefore assessment of the intracranial aneurysm along the entire course is of great clinical importance. Given the outstanding performance of artificial intelligence (AI) in image-based tasks, many AI-based applications have emerged in recent years for the assessment of intracranial aneurysms. In this review we will summarize the state-of-the-art of AI applications in intracranial aneurysms, emphasizing the achievements, and exploring the challenges. We will also discuss the future prospects and potential opportunities. This article provides an updated view of the AI applications in intracranial aneurysms and may act as a basis for guiding the related future works.

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

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

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information.

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.

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.

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.

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.