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DOI: 10.4103/ijri.IJRI_483_20
Retrospective analysis of chest HRCT findings in coronavirus disease pandemic (COVID-19)- An early experience
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Abstract
Context: Corona virus disease (COVID-19) has spread in a rampant manner all over the World causing severe acute respiratory illness. Prompt recognition of disease is invaluable to ensure timely treatment, and rapid patient isolation is crucial for containment of this communicable disease. Aims: To evaluate the performance of high resolution CT (HRCT) in the diagnosis of COVID-19. To establish the role of HRCT in prognosis of affected patients and to assess severity of disease based on HRCT findings. Settings and Design: This retrospective study included 216 patients admitted to our institute with a diagnosis of COVID-19 infection confirmed by nucleic acid RT-PCR testing. Subjects and Methods: Patients underwent non contrast HRCT chest examination and images were reconstructed in thin 1.25 mm lung window. Images were evaluated for presence of lung lesions & CT severity score was assigned to each patient based on number of lung lobes involved. Results: Ground-glass opacities (GGOs) with or without consolidation, interlobular septal thickening, the “reversed halo” sign (Atoll sign) and GGO’s with rounded nodular pattern are common CT features of COVID-19 Conclusions: Typical peripheral subpleural distributions of GGO’s with bilateral asymmetrical lung involvement are characteristic features of COVID19 pneumonia. Chest HRCT can be a very useful and standard imaging method to assess the severity and progression of the disease and thereby optimizing the management of patients. With increasing case load all over the world HRCT can be used as a one stop radiological investigation for the diagnosis and prognosis of corona virus disease.
Keywords
Atoll sign - coronavirus - COVID-19 - crazy –paving - ground glass opacities - PPE - RT-PCR - SARS-CoV-2Publication History
Received: 09 June 2020
Accepted: 08 September 2020
Article published online:
13 July 2021
© 2021. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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