Quantitative Analysis of Hemodynamic Changes in Branch Arteries Covered by Flow Diverters.

BACKGROUND AND OBJECTIVES: Understanding post-treatment hemodynamic alterations and their association with the patency of covered branch arteries is limited. This study aims to identify hemodynamic changes after flow diverter stenting and investigate their correlation with the patency status of covered branch arteries. METHODS: All patients treated with pipeline embolization device for anterior cerebral artery aneurysms at our center between 2016 and 2020 were screened for inclusion. Quantitative digital subtraction angiography was used to analyze changes in hemodynamic parameters pre- and post-stenting. The patency status of covered branch arteries after stenting was categorized as either patent or flow impairment (defined as artery stenosis or occlusion). RESULTS: A total of 71 patients, encompassing 89 covered branch arteries, were enrolled. Flow impairment was observed in 11.2% (10/89) of the branches. The mean transit time and full width at half maximum (FWHM) in covered branches were significantly prolonged post-stenting (P = .004 and .023, respectively). Flow-impaired branch arteries exhibited hemodynamic shifts contrary to those in patent branch arteries. Specifically, flow-impaired branches showed marked reductions in time to peak, FWHM, and mean transit time (decreases of 32.8%, 32.6%, and 29%, respectively; P = .006, .002, and .002, respectively). Further multivariate analysis revealed that reductions in FWHM in the branches (odds ratio = 0.97, 95% CI: 0.95-0.99, P = .007) and smoking (odds ratio = 14.5, 95% CI: 1.39-151.76, P = .026) were independent predictors of flow impairment of covered branches. CONCLUSION: Pipeline embolization device stenting can cause a reduction in blood flow in branch arteries. Compared with patent branches, flow-impaired branches exhibit an increase in blood flow velocity after stenting. Smoking and ΔFWHM in the covered branches indicate flow impairment.

Assessment of Perfusion Volumes by a New Automated Software for Computed Tomography Perfusion.

INTRODUCTION: To compare the perfusion volumes assessed by a new automated CT perfusion (CTP) software iStroke with the circular singular value decomposition software RAPID and determine its predictive value for functional outcome in patients with acute ischaemic stroke (AIS) who underwent endovascular treatment (EVT). METHODS: Data on patients with AIS were collected from four hospitals in China. All patients received CTP followed by EVT with complete recanalisation within 24 hours of symptom onset. We evaluated the agreement of CTP measures between the two softwares by Spearman's rank correlation tests and kappa tests. Bland-Altman plots were used to evaluate the agreement of infarct core volume (ICV) on CTP and ground truth on diffusion-weighted imaging (DWI). Logistic regression models were used to test the association between ICV on these two softwares and functional outcomes. RESULTS: Among 326 patients, 228 had DWI examinations and 40 of them had infarct volume >70 mL. In all patients, the infarct core and hypoperfusion volumes on iStroke had a strong correlation with those on RAPID (ρ=0.68 and 0.66, respectively). The agreement of large infarct core (volume >70 mL) was substantial (kappa=0.73, p<0.001) between these two softwares. The ICV measured by iStroke and RAPID was significantly correlated with independent functional outcome at 90 days (p=0.009 and p<0.001, respectively). In patients with DWI examinations and those with an ICV >70 mL, the ICV of iStroke and RAPID was comparable on individual agreement with ground truth. CONCLUSION: The automatic CTP software iStroke is a reliable tool for assessing infarct core and mismatch volumes, making it clinically useful for selecting patients with AIS for acute reperfusion therapy in the extended time window.

Quantitative Assessment of Hemodynamics Associated With Embolization Degree in Brain Arteriovenous Malformations.

BACKGROUND AND OBJECTIVES: Grading systems, including the novel brain arteriovenous malformation endovascular grading scale (NBAVMES) and arteriovenous malformation embocure score (AVMES), predict embolization outcomes based on arteriovenous malformation (AVM) morphological features. The influence of hemodynamics on embolization outcomes remains unexplored. In this study, we investigated the relationship between hemodynamics and embolization outcomes. METHODS: We conducted a retrospective study of 99 consecutive patients who underwent transarterial embolization at our institution between 2012 and 2018. Hemodynamic features of AVMs were derived from pre-embolization digital subtraction angiography sequences using quantitative digital subtraction angiography. Multivariate logistic regression analysis was performed to determine the significant factors associated with embolization outcomes. RESULTS: Complete embolization (CE) was achieved in 17 (17.2%) patients, and near-complete embolization was achieved in 18 (18.2%) patients. A slower transnidal relative velocity (TRV, odds ratio [OR] = 0.71, P = .002) was significantly associated with CE. Moreover, higher stasis index of the drainage vein (OR = 16.53, P = .023), shorter transnidal time (OR = 0.15, P = .013), and slower TRV (OR = 0.9, P = .049) were significantly associated with complete or near-complete embolization (C/nCE). The area under the receiver operating characteristic curve for predicting CE was 0.87 for TRV, 0.72 for NBAVMES scores (ρ = 0.287, P = .004), and 0.76 for AVMES scores. The area under the receiver operating characteristic curve for predicting C/nCE was 0.77 for TRV, 0.61 for NBAVMES scores, and 0.75 for AVMES scores. Significant Spearman correlation was observed between TRV and NBAVMES scores and AVMES scores (ρ = 0.512, P < .001). CONCLUSION: Preoperative hemodynamic factors have the potential to predict the outcomes of AVM embolization. A higher stasis index of the drainage vein, slower TRV, and shorter transnidal time may indicate a moderate blood flow status or favorable AVM characteristics that can potentially facilitate embolization.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

Evaluation of Diffusion-Perfusion Mismatch in Acute Ischemic Stroke with a New Automated Perfusion-Weighted Imaging Software: A Retrospective Study.

INTRODUCTION: The aim of this study was to evaluate the accuracy of automated software (iStroke) on magnetic resonance (MR) apparent diffusion coefficient (ADC) and perfusion-weighted imaging (PWI) against ground truth in assessing infarct core, and compare the hypoperfusion volume and mismatch volume on iStroke with those on Food and Drug Administration-approved software (RAPID) in patients with acute ischemic stroke. METHODS: We used the single-volume decomposition method to develop the iStroke (iStroke; Beijing Tiantan Hospital, Beijing, China) software. Patients with ischemic stroke were collected from two educational hospitals in China with MR-PWI performed in the emergency department within 24 h of symptom onset. Infarct core volume was defined as ADC < 620 × 10-6 mm2/s and hypoperfusion volume was defined as Tmax > 6 s. We compared the accuracy of infarct core volume using iStroke and RAPID (iSchema View Inc, Menlo Park, CA) software with ground truth. RESULTS: We included 405 patients with acute ischemic stroke with MR ADC and PWI sequences. The infarct core volume on iStroke (median 2.43 ml, interquartile range [IQR] 0.60-10.32 ml) was not significantly different from the ground truth (median 2.89 ml, IQR 0.77-9.17 ml) (P = 0.07); Bland-Altman curves showed that the core volume of iStroke and RAPID software were comparable with each other on individual agreement with ground truth. The hypoperfusion volume and mismatch volume on iStroke were not statistically different from those on the RAPID software, respectively. In patients with large vessel occlusion (n = 74), the agreement between iStroke and RAPID was substantial (kappa = 0.76) according to DEFUSE 3 criteria (infarct core < 70 ml, mismatch volume ≥ 15 ml, and mismatch ratio ≥ 1.8). CONCLUSIONS: The iStroke automatic processing of ADC and PWI is a reliable software for the identification of diffusion-perfusion mismatch in acute ischemic stroke.