ArticleMagnetic resonance imaging2019
Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study.
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- Uncertainty-aware automated labeling of intracranial arteries using deep learning.BMC medical imaging · 2026Article
- Associations Between Hemodynamics, Shape and Atherosclerosis in M1 of Middle Cerebral Artery: Insights From 4-Dimensional Flow Magnetic Resonance Imaging in Patients With Ischemic Stroke.Journal of the American Heart Association · 2026Article
- Morphologic characteristics of distal intracranial arteries in relation to structural changes in the brain after chronic alcohol consumption.Scientific reports · 2025Article
- Estimating Flow Direction of Circle of Willis Using Dynamic Arterial Spin-Labeling MR Angiography.AJNR. American journal of neuroradiology · 2024Article
- Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.Scientific reports · 2023Article
- Association between morphologic features of intracranial distal arteries and brain atrophy indexes in cerebral small vessel disease: a voxel-based morphometry study.Frontiers in neurology · 2023Article
- Associations of intracranial artery length and branch number on non-contrast enhanced MRA with cognitive impairment in individuals with carotid atherosclerosis.Scientific reports · 2022Article
- Vessel length on SNAP MRA and TOF MRA is a potential imaging biomarker for brain blood flow.Magnetic resonance imaging · 2021Article
- Uncontrolled hypertension associates with subclinical cerebrovascular health globally: a multimodal imaging study.European radiology · 2021Article
- Quantitative Assessment of the Intracranial Vasculature of Infants and Adults Using iCafe (Intracranial Artery Feature Extraction).Frontiers in neurology · 2021Article
- Intracranial vascular feature changes in time of flight MR angiography in patients undergoing carotid revascularization surgery.Magnetic resonance imaging · 2021Article
- A novel algorithm for refining cerebral vascular measurements in infants and adults.Journal of neuroscience methods · 2020Article
- Quantitative assessment of the intracranial vasculature in an older adult population using iCafe.Neurobiology of aging · 2019Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAccurate and reliable vascular features extracted from 3D time-of-flight (TOF) magnetic resonance angiography (MRA) can help evaluate cerebral vascular diseases and conditions. The goal of this study was to evaluate the reproducibility of an intracranial artery feature extraction (iCafe) algorithm for quantitative analysis of intracranial arteries from TOF MRA.
methodsTwenty-four patients with known intracranial artery stenosis were recruited and underwent two separate MRA scans within 2 weeks of each other. Each dataset was blinded to associated imaging and clinical data and then processed independently using iCafe. Inter-scan reproducibility analysis was performed on the 24 pairs of scans while intra-/inter-operator reproducibility and stenosis detection were assessed on 8 individual MRA scans. After tracing the vessels visualized on TOF MRA, iCafe was used to automatically extract the locations with stenosis and eight other vascular features. The vascular features included the following six morphometry and two signal intensity features: artery length (total, distal, and proximal), volume, number of branches, average radius of the M1 segment of the middle cerebral artery, and average normalized intensity of all arteries and large vertical arteries. A neuroradiologist independently reviewed the images to identify locations of stenosis for the reference standard. Reproducibility of stenosis detection and vascular features was assessed using Cohen's kappa, the intra-class correlation coefficient (ICC), and within-subject coefficient of variation (CV).
resultsThe segment-based sensitivity of iCafe for stenosis detection ranged from 83.3-91.7% while specificity was 97.4%. Kappa values for inter-scan and intra-operator reproducibility were 0.73 and 0.77, respectively. All vascular features demonstrated excellent inter-scan and intra-operator reproducibility (ICC = 0.91-1.00, and CV = 1.21-8.78% for all markers), and good to excellent inter-operator reproducibility (ICC = 0.76-0.99, and CV = 3.27-15.79% for all markers).
conclusionIntracranial artery features can be reliably quantified from TOF MRA using iCafe to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.
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