To determine the variability in assessment of the principal inherent characteristics of intracranial aneurysms through the evaluation of interobserver variability for material with uniform quality.Blinded interpretations of a single set of cerebral arteriograms of 55 aneurysms were evaluated by several statistical approaches.Excellent correlations were found for the detection and measurement of aneurysms after adjusting for geometric distortion caused by magnification. Progressively decreasing correspondence was found for factors that characterized morphology, including, in order, determination of margins, assessment of accessory appendages, and identification of a neck.Correction for geometric distortion was the most critical factor that influenced uniform measurement of size. Standards for measurement and morphologic characteristics were subsequently established for use in the International Study of Unruptured Intracranial Aneurysms.

Unruptured intracranial aneurysms (UIAs) are increasingly detected and are an important health-care burden. We aimed to assess the prevalence of UIAs according to family history, comorbidity, sex, age, country, and time period.Through searches of PubMed, Embase, and Web of Science we updated our 1998 systematic review up to March, 2011. We calculated prevalences and prevalence ratios (PRs) with random-effects binomial meta-analysis. We assessed time trends with year of study as a continuous variable.We included 68 studies, which reported on 83 study populations and 1450 UIAs in 94 912 patients from 21 countries. The overall prevalence was estimated as 3·2% (95% CI 1·9-5·2) in a population without comorbidity, with a mean age of 50 years, and consisting of 50% men. Compared with populations without the comorbidity, PRs were 6·9 (95% CI 3·5-14) for autosomal dominant polycystic kidney disease (ADPKD), 3·4 (1·9-5·9) for a positive family history of intracranial aneurysm of subarachnoid haemorrhage, 3·6 (0·4-30) for brain tumour, 2·0 (0·9-4·6) for pituitary adenoma, and 1·7 (0·9-3·0) for atherosclerosis. The PR for women compared with men was 1·61 (1·02-2·54), with a ratio of 2·2 (1·3-3·6) in study populations with a mean age of more than 50 years. Compared with patients older than 80 years, we found no differences by age, except for patients younger than 30 years (0·01, 0·00-0·12). Compared with the USA, PRs were similar for other countries, including Japan (0·8, 0·4-1·7) and Finland (1·0, 0·4-2·4). There was no statistically significant time trend.The prevalence of UIAs is higher in patients with ADPKD or a positive family history of intracranial aneurysm of subarachnoid haemorrhage than in people without comorbidity. In Finland and Japan, the higher incidence of subarachnoid haemorrhage is not explained by a higher prevalence of UIAs, implicating higher risks of rupture.Julius Centre for Health Sciences and Primary Care and Department of Neurology and Neurosurgery, University Medical Centre, Utrecht.Copyright © 2011 Elsevier Ltd. All rights reserved.

从脑部医学影像中划分动脉树区域是诊断和评估许多脑血管疾病的早期步骤．现有的区域分割方法多依赖人工辅助，本文中提出了一种基于双分支连通网络（dual branch connected network，DBCNet）的脑动脉树自动分区方法，可以将时间飞跃磁共振血管造影（time of flight-magnetic resonance angiography，TOF-MRA）中的动脉树分割为6个主要区域．DBCNet中引入了分支特征解耦模块和Swin Transformer机制的全局与局部特征融合模块，训练采用先定位后分割的两步训练策略．本研究使用了111例TOF-MRA数据，其中81例作为训练集，20例作为验证集，10例作为测试集，模型在测试集上的平均Dice系数为74.72%，95%豪斯多夫距离（HD95）为3.89 mm．和其他先进分割网络相比较，该网络能更准确地分割出各个主要区域，并具有一定的鲁棒性．

Time of flight magnetic resonance angiography (TOF-MRA) is the primary non-invasive screening method for cerebral aneurysms. We aimed to develop a computer-aided aneurysm detection method to improve the diagnostic efficiency and accuracy, especially decrease the false positive rate.This is a retrospective multicenter study. The dataset contained 1160 TOF-MRA examinations composed of unruptured aneurysms (n = 1096) and normal controls (n = 166) from six hospitals. A total of 1037 examinations acquired from 2013 to 2019 were used as training set; 123 examinations acquired from 2020 to 2021 were used as external test set. We proposed an equalized augmentation strategy based on aneurysm location and constructed a detection model based on dual channel SE-3D UNet. The model was trained with a 5-fold cross-validation in the training set, then tested on the external test set.The proposed method achieved 82.46% sensitivity on patient-level, 73.85% sensitivity on lesion-level, and 0.88 false positives per case in the external test set. The performance did not show significant differences in subgroups according to the aneurysm site (except ACA), aneurysm size (except smaller than 3 mm), or MRI scanners. The performance preceded the basic SE-3D UNet by increasing 15.79% patient-level sensitivity and decreasing 4.19 FPs/case.The proposed automated aneurysm detection method achieved acceptable sensitivity while controlling fairly low false positives per case. It might provide a useful auxiliary tool of cerebral aneurysms MRA screening.• The need for automated cerebral aneurysms detecting is growing. • The strategy of equalized augmentation based on aneurysm location and dual-channel input could improve the model performance. • The retrospective multi-center study showed that the proposed automated cerebral aneurysms detection using dual-channel SE-3D UNet could achieve acceptable sensitivity while controlling a low false positive rate.© 2023. The Author(s), under exclusive licence to European Society of Radiology.

Blebs are rupture risk factors in intracranial aneurysms (IAs), but their prevalence, distribution, and associations with clinical factors as well as their causes and effects on aneurysm vulnerability remain unclear.A total of 122 blebs in 270 IAs selected for surgery were studied using patient-specific vascular reconstructions from 3D angiographic images. Bleb geometry, location on the aneurysm, and frequency of occurrence in aneurysms at different locations were analyzed. Associations between gender, age, smoking, hypertension, hormone therapy, dental infection, and presence of blebs were investigated.Of all aneurysms with blebs, 77% had a single bleb and 23% had multiple blebs. Only 6% of blebs were at the neck, while 46% were in the body and 48% in the dome. Aneurysms with blebs were larger (p<0.0001), more elongated (p=0.0002), and with wider necks than aneurysms without blebs. Bleb presence was associated with dental infection (p=0.0426) and negatively associated with hormone therapy (p=0.0426) in women. Anterior and posterior communicating arteries had larger percentages of aneurysms with blebs than internal carotid arteries. Patients with a history of hypertension tended to have a larger percentage of aneurysms with blebs. However, these trends did not reach significance in this sample.Blebs are common in IAs, and most aneurysms harboring blebs have a single bleb. Blebs in the aneurysm neck are rare, but they are equally common in the body and dome. The presence of blebs in IAs was associated with dental infection, and negatively associated with hormone replacement therapy.© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. © RSNA, 2018 See also the editorial by Flanders in this issue.

脑动脉瘤破裂造成的蛛网膜下腔出血致死致残率极高，借助深度学习网络辅助医生实现高效筛查具有重要意义．为提高基于时间飞跃法磁共振血管造影（Time of Flight-Magnetic Resonance Angiography，TOF-MRA）的脑动脉瘤自动检测的精度，本文基于模糊标签方式，提出一种基于变体3D U-Net和双分支通道注意力（Dual-branch Channel Attention，DCA）的深度神经网络DCAU-Net，DCA模块可以自适应地调整通道特征的响应，提高特征提取能力．首先对260例病例的TOF-MRA影像预处理，将数据集分为174例训练集、43例验证集和43例测试集，然后使用处理后的数据训练和验证DCAU-Net，测试集实验结果表明DCAU-Net可以达到90.69%的敏感度，0.83个/例的假阳性计数和0.52的阳性预测值，有望为动脉瘤筛查提供参考．