Advanced cross-sectional imaging of cerebral aneurysms

Nonlinear biomechanical behaviour of extracranial carotid artery aneurysms in the framework of Windkessel effect via FSI technique

Extracranial carotid artery aneurysms (ECCA) lead to rupture and neurologic symptoms from embolisation, with potentially fatal outcomes. Investigating the biomechanical behaviour of EECA with blood flow dynamics is crucial for identifying regions more susceptible to rupture. A coupled three-dimensional (3D) Windkessel-framework and hyperelastic fluid-structure interaction (FSI) analysis of ECCAs with patient-specific geometries, was developed in this paper with a particular focus on hemodynamic parameters and the arterial wall's biomechanical response. The blood flow has been modelled as non-Newtonian, pulsatile, and turbulent. The biomechanical characteristics of the aneurysm and artery are characterised employing a 5-parameter Mooney-Rivlin hyperelasticity model. The Windkessel effect is also considered to efficiently simulate pressure profile of the outlets and to capture the dynamic changes over the cardiac cycle. The study found the aneurysm carotid artery exhibited the high levels of pressure, wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT) compared to the healthy one. The deformation of the arterial wall and the corresponding von Mises (VM) stress were found significantly increased in aneurysm cases, in comparison to that of no aneurysm cases, which strongly correlated with the hemodynamic characteristics of the blood flow and the geometric features of the aneurysms. This escalation would intensify the risk of aneurysm wall rupture. These findings have critical implications for enhancing treatment strategies for patients with extracranial aneurysms.

Advancements in imaging of intracranial atherosclerotic disease: beyond the arterial lumen to the vessel wall

Intracranial atherosclerotic disease (ICAD) significantly increases the risk of ischemic stroke. It involves the accumulation of plaque within arterial walls and narrowing or blockage of blood vessel lumens. Accurate imaging is crucial for the diagnosis and management of ICAD at both acute and chronic stages. However, imaging the small, tortuous intracranial arterial walls amidst complex structures is challenging. Clinicians have employed diverse approaches to improve imaging quality, with a particular emphasis on optimizing the acquisition of images using new techniques, enhancing spatial and temporal resolution of images, and refining post-processing techniques. ICAD imaging has evolved from depicting lumen stenosis to assessing blood flow reserve and identifying plaque components. Advanced techniques such as fractional flow reserve (FFR), high-resolution vessel wall magnetic resonance (VW-MR), optical coherence tomography (OCT), and radial wall strain (RWS) now allow direct visualization of flow impairment, vulnerable plaques, and blood flow strain to plaque, aiding in the selection of high-risk stroke patients for intervention. This article reviews the progression of imaging modalities from lumen stenosis to vessel wall pathology and compares their diagnostic value for risk stratification in ICAD patients.

Risk factors and predictive indicators of rupture in cerebral aneurysms

Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.

Quantifying irregular pulsation of intracranial aneurysms using 4D-CTA

Recent studies have suggested that irregular pulsation of intracranial aneurysm during the cardiac cycle may be potentially associated with aneurysm rupture risk. However, there is a lack of quantification method for irregular pulsations. This study aims to quantify irregular pulsations by the displacement and strain distribution of the intracranial aneurysm surface during the cardiac cycle using four-dimensional CT angiographic image data. Four-dimensional CT angiography was performed in 8 patients. The image data of a cardiac cycle was divided into approximately 20 phases, and irregular pulsations were detected in four intracranial aneurysms by visual observation, and then the displacement and strain of the intracranial aneurysm was quantified using coherent point drift and finite element method. The displacement and strain were compared between aneurysms with irregular and normal pulsations in two different ways (total and stepwise). The stepwise first principal strain was significantly higher in aneurysms with irregular than normal pulsations (0.20±0.01 vs 0.16±0.02, p=0.033). It was found that the irregular pulsations in intracranial aneurysms usually occur during the consecutive ascending or descending phase of volume changes during the cardiac cycle. In addition, no statistically significant difference was found in the aneurysm volume changes over the cardiac cycle between the two groups. Our method can successfully quantify the displacement and strain changes in the intracranial aneurysm during the cardiac cycle, which may be proven to be a useful tool to quantify intracranial aneurysm deformability and aid in aneurysm rupture risk assessment.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study

PURPOSE

To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA).

METHOD

This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability.

RESULTS

510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06.

CONCLUSIONS

The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

Report from the society of magnetic resonance angiography: clinical applications of 7T neurovascular MR in the assessment of intracranial vascular disease

In recent years, ultra-high-field magnetic resonance imaging (MRI) applications have been rapidly increasing in both clinical research and practice. Indeed, 7-Tesla (7T) MRI allows improved depiction of smaller structures with high signal-to-noise ratio, and, therefore, may improve lesion visualization, diagnostic capabilities, and thus potentially affect treatment decision-making. Incremental evidence emerging from research over the past two decades has provided a promising prospect of 7T magnetic resonance angiography (MRA) in the evaluation of intracranial vasculature. The ultra-high resolution and excellent image quality of 7T MRA allow us to explore detailed morphological and hemodynamic information, detect subtle pathological changes in early stages, and provide new insights allowing for deeper understanding of pathological mechanisms of various cerebrovascular diseases. However, along with the benefits of ultra-high field strength, some challenges and concerns exist. Despite these, ongoing technical developments and clinical oriented research will facilitate the widespread clinical application of 7T MRA in the near future. In this review article, we summarize technical aspects, clinical applications, and recent advances of 7T MRA in the evaluation of intracranial vascular disease. The aim of this review is to provide a clinical perspective for the potential application of 7T MRA for the assessment of intracranial vascular disease, and to explore possible future research directions implementing this technique.

Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques

The treatment of intracranial aneurysms is dictated by its risk of rupture in the future. Several clinical and radiological risk factors for aneurysm rupture have been described and incorporated into prediction models. Despite the recent technological advancements in aneurysm imaging, linear length and visible irregularity with a bleb are the only radiological measure used in clinical prediction models. The purpose of this article is to summarize both the standard imaging techniques, including their limitations, and the advanced techniques being used experimentally to image aneurysms. It is expected that as our understanding of advanced techniques improves, and their ability to predict clinical events is demonstrated, they become an increasingly routine part of aneurysm assessment. It is important that neurovascular specialists understand the spectrum of imaging techniques available.

MR Imaging of the Cerebral Aneurysmal Wall for Assessment of Rupture Risk

The evaluation of unruptured intracranial aneurysms requires a comprehensive and multifaceted approach. The comprehensive analysis of aneurysm wall enhancement through high-resolution MRI, in tandem with advanced processing techniques like finite element analysis, quantitative susceptibility mapping, and computational fluid dynamics, has begun to unveil insights into the intricate biology of aneurysms. This enhanced understanding of the etiology, progression, and eventual rupture of aneurysms holds the potential to be used as a tool to triage patients to intervention versus observation. Emerging tools such as radiomics and machine learning are poised to contribute significantly to this evolving landscape of diagnostic refinement.

Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA

BACKGROUND AND OBJECTIVE

Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment.

METHODS

The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data.

RESULTS

The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points.

CONCLUSIONS

Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation.

The biomechanical effects of different membrane layer structures and material constitutive modeling on patient-specific cerebral aneurysms

The prevention, control and treatment of cerebral aneurysm (CA) has become a common concern of human society, and by simulating the biomechanical environment of CA using finite element analysis (FEA), the risk of aneurysm rupture can be predicted and evaluated. The target models of the current study are mainly idealized single-layer linear elastic cerebral aneurysm models, which do not take into account the effects of the vessel wall structure, material constitution, and structure of the real CA model on the mechanical parameters. This study proposes a reconstruction method for patient-specific trilaminar CA structural modeling. Using two-way fluid-structure interaction (FSI), we comparatively analyzed the effects of the differences between linear and hyperelastic materials and three-layer and single-layer membrane structures on various hemodynamic parameters of the CA model. It was found that the numerical effects of the different CA membrane structures and material constitution on the stresses and wall deformations were obvious, but does not affect the change in its distribution pattern and had little effect on the blood flow patterns. For the same material constitution, the stress of the three-layer membrane structure were more than 10.1% larger than that of the single-layer membrane structure. For the same membrane structure, the stress of the hyperelastic material were more than 5.4% larger than that of the linear elastic material, and the displacement of the hyperelastic material is smaller than that of the linear elastic material by about 20%. And the maximum value of stress occurred in the media, and the maximum displacement occurred in the intima. In addition, the upper region of the tumor is the maximum rupture risk region for CA, and the neck of the tumor and the bifurcation of the artery are also the sub-rupture risk regions to focus on. This study can provide data support for the selection of model materials for CA simulation and analysis, as well as a theoretical basis for clinical studies and subsequent research methods.