articleTop 1% cited

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

European Radiology · 2019 · Vol. 29(11) · pp. 6163–6171

Motonori Akagi✉(Hiroshima University)Yuko Nakamura(Hiroshima University)Toru Higaki(Hiroshima University)Keigo Narita(Hiroshima University)Yukiko Honda(Hiroshima University)Jian Zhou(Canon (United States))Yu Zhou(Canon (United States))Naruomi Akino(Canon (Japan))Kazuo Awai(Hiroshima University)

View via DOI Publisher page

Radiation Dose and Imaging Advanced X-ray and CT Imaging Medical Imaging Techniques and ApplicationsImage qualityMedicineIterative reconstructionImage noiseNoise (video)NeuroradiologyRadiologyImage resolutionArtificial intelligenceNuclear medicine

MeSH terms

AbdomenDeep LearningAdultAgedAged, 80 and overAlgorithmsFemaleHumansLiver NeoplasmsMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesTomography, X-Ray ComputedReproducibility of Results

Citations

342

FWCI

32.45

field-weighted impact

References

Percentile

100%

vs. same field & year

Citations per year

Cited by

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study