article Open AccessTop 1% cited
Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study
European Radiology · 2020 · Vol. 30(7) · pp. 3951–3959
Joël Greffier✉(Université de Montpellier)Aymeric Hamard(Université de Montpellier)Fabrício Pereira(Université de Montpellier)C. Barrau(Université de Montpellier)H. Pasquier(General Electric (France))Jean Paul Beregi(Université de Montpellier)Julien Frandon(Université de Montpellier)
Radiation Dose and ImagingAdvanced X-ray and CT ImagingMedical Imaging Techniques and ApplicationsImaging phantomImage qualityIterative reconstructionAlgorithmImage resolutionImage noiseNoise reductionNuclear medicineNoise (video)Artificial intelligence
MeSH terms
Deep LearningAlgorithmsHumansRadiation DosageRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedPhantoms, Imaging
Citations
317
FWCI
22.38
field-weighted impact
References
35
Percentile
100%
vs. same field & year
Citations per year
References
A three‐dimensional statistical approach to improved image quality for multislice helical CT
Medical Physics · 2007 · 932 citations
Computed Tomography — An Increasing Source of Radiation Exposure
New England Journal of Medicine · 2007 · 8,611 citations
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
IEEE Transactions on Medical Imaging · 2016 · 681 citations
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
European Radiology · 2018 · 573 citations
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
European Radiology · 2019 · 342 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.