Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature.
3D angiography
Artificial intelligence
Deep learning
Dose reduction
Flat-detector computed tomography
Journal
Clinical neuroradiology
ISSN: 1869-1447
Titre abrégé: Clin Neuroradiol
Pays: Germany
ID NLM: 101526693
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
08
05
2019
accepted:
28
08
2019
pubmed:
11
10
2019
medline:
27
10
2021
entrez:
11
10
2019
Statut:
ppublish
Résumé
The 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. 3D 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). In 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 The 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.
Identifiants
pubmed: 31598760
doi: 10.1007/s00062-019-00836-7
pii: 10.1007/s00062-019-00836-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
705-712Références
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