Graph convolutional networks for automated intracranial artery labeling.

Purpose: Unruptured intracranial aneurysms (UIAs) can cause aneurysmal subarachnoid hemorrhage, a severe and often lethal type of stroke. Automated labeling of intracranial arteries can facilitate the identification of risk factors associated with UIAs. This study aims to improve intracranial artery labeling using atlas-based features in graph convolutional networks. Approach: We included three-dimensional time-of-flight magnetic resonance angiography scans from 150 individuals. Two widely used graph convolutional operators, GCNConv and GraphConv, were employed in models trained to classify 12 bifurcations of interest. Cross-validation was applied to explore the effectiveness of atlas-based features in node classification. The results were tested for statistically significant differences using a Wilcoxon signed-rank test. Model repeatability and calibration were assessed on the test set for both operators. In addition, we evaluated model interpretability and node feature contribution using explainable artificial intelligence. Results: Atlas-based features led to statistically significant improvements in node classification (p<0.05). The results showed that the best discrimination and calibration performances were obtained using the GraphConv operator, which yielded a mean recall of 0.87, precision of 0.90, and expected calibration error of 0.02. Conclusions: The addition of atlas-based features improved node classification results. The GraphConv operator, which incorporates higher-order structural information during training, is recommended over the GCNConv operator based on the accuracy and calibration of predicted outcomes.

Three-Dimensional Morphological Change of Intracranial Aneurysms Before and Around Rupture.

BACKGROUND AND OBJECTIVES: Patients with an unruptured intracranial aneurysm often undergo periodic imaging to detect potential aneurysm growth, which is associated with an increased rupture risk. Because prediction of rupture based on growth is moderate, morphological changes have gained interest as a risk factor for rupture. We studied 3-dimensional-quantified morphological changes over time during radiological monitoring before rupture and around rupture. METHODS: In this retrospective observational study, we identified aneurysms that ruptured during follow-up, with imaging available for at least 2 time points before rupture and one after rupture. For each time point, we obtained 8 morphological parameters: 2-dimensional size, volume, surface area, compactness 1 and 2, sphericity, elongation, and flatness. Morphological changes before rupture and around rupture were log-transformed, scaled, and analyzed with linear mixed-effects models. RESULTS: We included 16 aneurysms in 16 patients who were imaged between 2004 and 2021. In the time period before rupture (median follow-up duration 1200 days, IQR 736-1340), 3 size-related morphological parameters increased: 2-dimensional size (estimated mean change 0.44, 95% CI 0.24-0.65), volume (estimated mean change 0.34, 95% CI 0.12-0.56), and surface area (0.33, 95% CI 0.11-0.54). In the period around rupture (median follow-up duration 407 days, IQR 148-719), these parameters further increased. In addition, 5 morphological parameters (compactness 1 and 2, sphericity, elongation, and flatness) decreased around rupture but not before rupture. CONCLUSION: Change in aneurysm volume and surface area may be novel risk factors for rupture. Because most morphological parameters changed around but not before rupture, morphological changes during these 2 periods should be regarded as different processes. This implies that postrupture morphology should not be used as a surrogate for prerupture morphology in rupture prediction models.

Hemodynamic Parameters in the Parent Arteries of Unruptured Intracranial Aneurysms Depend on Aneurysm Size and Are Different Compared to Contralateral Arteries: A 7 Tesla 4D Flow MRI Study.

BACKGROUND: Different Circle of Willis (CoW) variants have variable prevalences of aneurysm development, but the hemodynamic variation along the CoW and its relation to presence and size of unruptured intracranial aneurysms (UIAs) are not well known. PURPOSE: Gain insight into hemodynamic imaging markers of the CoW for UIA development by comparing these outcomes to the corresponding contralateral artery without an UIA using 4D flow magnetic resonance imaging (MRI). STUDY TYPE: Retrospective, cross-sectional study. SUBJECTS: Thirty-eight patients with an UIA, whereby 27 were women and a mean age of 62 years old. FIELD STRENGTH/SEQUENCE: Four-dimensional phase-contrast (PC) MRI with a 3D time-resolved velocity encoded gradient echo sequence at 7 T. ASSESSMENT: Hemodynamic parameters (blood flow, velocity pulsatility index [vPI], mean velocity, distensibility, and wall shear stress [peak systolic (WSSMAX ), and time-averaged (WSSMEAN )]) in the parent artery of the UIA were compared to the corresponding contralateral artery without an UIA and were related to UIA size. STATISTICAL TESTS: Paired t-tests and Pearson Correlation tests. The threshold for statistical significance was P < 0.05 (two-tailed). RESULTS: Blood flow, mean velocity, WSSMAX , and WSSMEAN were significantly higher, while vPI was lower, in the parent artery relative to contralateral artery. The WSSMAX of the parent artery significantly increased linearly while the WSSMEAN decreased linearly with increasing UIA size. CONCLUSIONS: Hemodynamic parameters and WSS differ between parent vessels of UIAs and corresponding contralateral vessels. WSS correlates with UIA size, supporting a potential hemodynamic role in aneurysm pathology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Geometric Deep Learning Using Vascular Surface Meshes for Modality-Independent Unruptured Intracranial Aneurysm Detection.

Early detection of unruptured intracranial aneurysms (UIAs) enables better rupture risk and preventative treatment assessment. UIAs are usually diagnosed on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRA) or contrast-enhanced Computed Tomography Angiographs (CTA). Various automatic voxel-based deep learning UIA detection methods have been developed, but these are limited to a single modality. We propose a modality-independent UIA detection method using a geometric deep learning model with high resolution surface meshes of brain vessels. A mesh convolutional neural network with ResU-Net style architecture was used. UIA detection performance was investigated with different input and pooling mesh resolutions, and including additional edge input features (shape index and curvedness). Both a higher resolution mesh (15,000 edges) and additional curvature edge features improved performance (average sensitivity: 65.6%, false positive count/image (FPC/image): 1.61). UIAs were detected in an independent TOF-MRA test set and a CTA test set with average sensitivity of 52.0% and 48.3% and average FPC/image of 1.04 and 1.05 respectively. We provide modality-independent UIA detection using a deep-learning vascular surface mesh model with comparable performance to state-of-the-art UIA detection methods.

Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge.

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset. A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics. In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/.