Cerebral aneurysms should be treated on the basis of accurate rupture risk prediction. Nowadays, the rupture risk in aneurysms has been estimated using hemodynamic parameters. In this paper, we suggest a new way to predict the rupture risks in cerebral aneurysms by using fluid–structure interaction (FSI) analysis for better decision-making regarding treatment. A patient-specific model was constructed using digital subtraction angiography of 51 cerebral aneurysms. For each model, a thin-walled area (TWA) was first predicted using computational fluid dynamics (CFD), and then the highest equivalent strain in the TWA was calculated with FSI by varying wall thicknesses and mechanical properties. A critical curve was made from 16 FSI results for each patient-specific model to estimate the rupture risk. On average, the equivalent strains of the ruptured aneurysms were higher than those of the unruptured aneurysms. Furthermore, the patterns of critical curves between unruptured and ruptured aneurysms were clearly distinguishable. From the rupture risk evaluation based on the cut-off value, 24 of the 27 unruptured aneurysms and 15 of the 24 ruptured aneurysms were matched with actual-clinical setting cases. The critical curve proposed in the present study could be an effective tool for the prediction of the rupture risk of aneurysm.

OBJECTIVE: Rete middle cerebral artery (MCA) is extremely rare and has not been frequently discussed. Rete MCA is a weblike anomaly of the MCA that does not coalesce and forms a prominent, large single branch from the plexiform vessels in the fetal stage. The purpose of this study was to further elucidate the clinical and radiological characteristics of patients with rete MCA.METHODS: A total of 2262 cerebral digital subtraction angiography procedures were performed on 1937 patients at the authors’ institution from February 2013 to May 2017. Data analysis included age, sex, clinical symptoms, underlying diseases, coexisting cerebral arterial anomalies, and operative methods and findings.RESULTS: Rete MCAs were found in 13 patients, and the incidence of this anomaly was 0.67% (13 of 1937) in this study. Of the 13 patients, 3 had hemorrhagic strokes, 6 had ischemic strokes, and 4 had no symptoms. Eight patients underwent conservative treatment, and 5 patients underwent surgical treatment. Rete MCA is considered a congenital disease of the cerebral vasculature with the possibility of an acquired abnormality, such as an aneurysm, caused by hemodynamic stress. Although an epidemiological survey of rete MCA was not conducted, it is assumed that rete MCA has a high prevalence in Asia. Ischemic and hemorrhagic stroke events are fairly common in rete MCA.CONCLUSIONS: Clinicians should understand the radiological and clinical features of patients with rete MCA to avoid misdiagnosis and unnecessary treatment. This anomaly should be differentiated from other vascular diseases and patients presenting incidentally should be carefully monitored because of their vulnerability to both hemorrhagic and ischemic strokes.

Background Cerebral aneurysms should be treated before rupture because ruptured aneurysms result in serious disability. Therefore, accurate prediction of rupture risk is important and has been estimated using various hemodynamic factors. Objective To suggest a new way to predict rupture risk in cerebral aneurysms using a novel deep learning model based on hemodynamic parameters for better decision-making about treatment. Methods A novel convolutional neural network (CNN) model was used for rupture risk prediction retrospectively of 123 aneurysm cases. To include the effect of hemodynamic parameters into the CNN, the hemodynamic parameters were first calculated using computational fluid dynamics and fluid–structure interaction. Then, they were converted into images for training the CNN using a novel approach. In addition, new data augmentation methods were devised to obtain sufficient training data. A total of 53,136 images generated by data augmentation were used to train and test the CNN. Results The CNNs trained with wall shear stress (WSS), strain, and combination images had area under the receiver operating characteristics curve values of 0.716, 0.741, and 0.883, respectively. Based on the cut-off values, the CNN trained with WSS (sensitivity: 0.5, specificity: 0.79) or strain (sensitivity: 0.74, specificity: 0.71) images alone was not highly predictive. However, the CNN trained with combination images of WSS and strain showed a sensitivity and specificity of 0.81 and 0.82, respectively. Conclusion CNN-based deep learning algorithm using hemodynamic factors, including WSS and strain, could be an effective tool for predicting rupture risk in cerebral aneurysms with good predictive accuracy.

Among the various perspectives on cerebrovascular diseases, hemodynamic analysis—which has recently garnered interest—is of great help in understanding cerebrovascular diseases. Computational fluid dynamics (CFD) analysis has been the primary hemodynamic analysis method, and studies on cerebral aneurysms have been actively conducted. However, owing to the intrinsic limitations of the analysis method, the role of wall shear stress (WSS), the most representative parameter, remains controversial. High WSS affects the formation of cerebral aneurysms; however, no consensus has been reached on the role of WSS in the growth and rupture of cerebral aneurysms. Therefore, this review aimed to briefly introduce the up-to-date results and limitations made through CFD analysis and to inform the need for a new hemodynamic analysis method.

Object. Rupture of a cerebral aneurysm occurs mainly in a thin-walled area (TWA). Prediction of TWAs would help to assess the risk of rupture and select appropriate treatment strategy. There are several limitations of current prediction techniques for TWAs. To predict TWAs more accurately, HP should be normalized to minimize the influence of analysis conditions, and the effectiveness of normalized, combined hemodynamic parameters (CHPs) should be investigated with help of the quantitative color analysis of intraoperative images. Methods. A total of 21 unruptured cerebral aneurysms in 19 patients were analyzed. A normalized CHP was newly suggested as a weighted average of normalized wall shear stress (WSS) and normalized oscillatory shear index (OSI). Delta E from International Commission on Illumination was used to more objectively quantify color differences in intraoperative images. Results. CFD analysis results indicated that WSS and OSI were more predictive of TWAs than pressure (P<.001, P=.187, P=.970, respectively); these two parameters were selected to define the normalized CHP. The normalized CHP became more statistically significant (P<.001) as the weighting factor of normalized WSS increased and that of normalized OSI decreased. Locations with high CHP values corresponded well to those with high Delta E values (P<.001). Predicted TWAs based on the normalized CHP showed a relatively good agreement with intraoperative images (17 in 21 cases, 81.0%). Conclusion. 100% weighting on the normalized WSS produced the most statistically significant result. The normalization scheme for WSS and OSI suggested in this work was validated using quantitative color analyses, rather than subjective judgments, of intraoperative images, and it might be clinically useful for predicting TWAs of unruptured cerebral aneurysms. The normalization scheme would also be integrated into further fluid-structure interaction analysis for more reliable estimation of the risk of aneurysm rupture.

In the hemodynamic study, computational fluid dynamics (CFD) analysis has shown that high wall shear stress (WSS) is an important parameter in cerebral aneurysm formation. However, CFD analysis is not more realistic than fluid–structure interaction (FSI) analysis given its lack of considering the involvement of vascular structures. To investigate the relationship between the hemodynamic parameters and the aneurysm formation, the locations of high WSS and high strain were extracted from the CFD and FSI analyses, respectively. Then the distances between the aneurysm formation site and the locations of high WSS or high strain were calculated. A total of 37 intracranial paraclinoid aneurysms were enrolled for quantitative comparison. Additionally, the dura mater was modeled to facilitate realistic results in FSI analysis. The average distance from the location of the aneurysm formation site to the high strain (1.74 mm $$\pm $$± 1.04 mm) was smaller than the average distance to the high WSS (3.33 mm $$\pm $$± 1.18 mm). The presence of dura mater also influenced the findings in the aneurysm formation site. High strain extracted by FSI analysis is an important hemodynamic factor related to the formation of cerebral aneurysms. Strain parameter could help to predict the formation of aneurysms and elucidate the appropriate treatment.