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Cited by 505

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know

Open access
Kooraki, Soheil, Hosseiny, Melina, Myers, Lee, Gholamrezanezhad, Ali·2020·Journal of the American College of Radiology

Abstract

In December 2019, a novel coronavirus (COVID-19) pneumonia emerged in Wuhan, China. Since then, this highly contagious COVID-19 has been spreading worldwide, with a rapid rise in the number of deaths. Novel COVID-19–infected pneumonia (NCIP) is characterized by fever, fatigue, dry cough, and dyspnea. A variety of chest imaging features have been reported, similar to those found in other types of coronavirus syndromes. The purpose of the present review is to briefly discuss the known epidemiology and the imaging findings of coronavirus syndromes, with a focus on the reported imaging findings of NCIP. Moreover, the authors review precautions and safety measures for radiology department personnel to manage patients with known or suspected NCIP. Implementation of a robust plan in the radiology department is required to prevent further transmission of the virus to patients and department staff members.

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Metadata

Authors
Kooraki, S., Hosseiny, M., Myers, L., & Gholamrezanezhad, A.
Venue
Journal of the American College of Radiology
Publisher
Elsevier BV
Published
2020-04
Volume
17
Issue
4
Pages
447-451
ISSN
1546-1440 (ISSN-L)
OpenAlex
W3007678840
Cited by
505
References
4
FWCI
14.24
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know. https://app.cochat.ai/papers/10.1016/j.jacr.2020.02.008
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Kooraki, S., Hosseiny, M., Myers, L., & Gholamrezanezhad, A. (2020). Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know. Journal of the American College of Radiology. https://doi.org/10.1016/j.jacr.2020.02.008

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.