ArticleClinical neuroradiology2020
Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature.
Article in Clinical neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Fractal analysis of healthy and diseased vasculature in pediatric Moyamoya disease.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Calculation of virtual 3D subtraction angiographies using conditional generative adversarial networks (cGANs).BMC medical imaging · 2024Article
- Application of deblur technology for improving the clarity of digital subtractive angiography.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2024Article
- Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review.Cureus · 2024Review
- Design of urban innovation space system using artificial intelligence technology and internet of things.Heliyon · 2024Article
- Clinical Efficiency of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Aneurysm: Comparison with the Conventional Method.Clinical neuroradiology · 2023Article
- The Utility of Multimodal Imaging and Artificial Intelligence Algorithms for Overlying Two Volumes in the Decision Chain for the Treatment of Complex Pathologies in Interventional Neuroradiology-A Case Series Study.Life (Basel, Switzerland) · 2023Article
- Accuracy of Dose-Saving Artificial-Intelligence-Based 3D Angiography (3DA) for Grading of Intracranial Artery Stenoses: Preliminary Findings.Diagnostics (Basel, Switzerland) · 2023Article
- Construction and imaging of a neurovascular unit model.Neural regeneration research · 2022Review
- Review
- Low-Dose Three-Dimensional Rotational Angiography for Evaluating Intracranial Aneurysms: Analysis of Image Quality and Radiation Dose.Korean journal of radiology · 2022Article
- The Value of CT Angiography Based on Intelligent Segmentation Algorithm for Survival of Hemodialysis Patients.Computational and mathematical methods in medicine · 2022Article
- Artificial Intelligence-Based 3D Angiography for Visualization of Complex Cerebrovascular Pathologies.AJNR. American journal of neuroradiology · 2021Article
- Relaxation-Enhanced Angiography Without Contrast and Triggering (REACT) for Fast Imaging of Extracranial Arteries in Acute Ischemic Stroke at 3 T.Clinical neuroradiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was developed to reduce radiation dosage because only one contrast-enhanced run of the C‑arm system is required for reconstruction of DSA-like 3D volumes. The aim was the evaluation of this algorithm regarding its diagnostic information.
methods3D DSA datasets without pathologic findings were reconstructed both with subtraction technique and with the AI-based algorithm. Corresponding reconstructions were evaluated by 2 neuroradiologists with respect to image quality (IQ), visualization of major segments of the circle of Willis (ICA = C4-C7; OphA; ACA = A1-A2, MCA = M1-M2; VA = V4; BA; AICA; SUCA; PCA = P1-P2), identifiability of perforators (lenticulostriate/thalamoperforating arteries) and vessel diameters (ICA = C4; MCA = M1; BA; PCA = P1).
resultsIn total 15 datasets were successfully reconstructed as 3D DSA and 3DA with diagnostic image quality. All major segments of the circle of Willis and perforators were comparably visualized with 3DA. Quantitative analysis of vessel diameters in 3D DSA and 3DA datasets was equivalent and did not show relevant differences (r
conclusionsThe use of 3DA demonstrated reliable visualization of cerebral vasculature with respect to quantitative and qualitative parameters. Therefore, 3DA is a promising method that might help to reduce patient radiation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.