Determining the Spectrum of High-Resolution Computed Tomography Chest Findings in Patients Diagnosed with COVID 19

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Abstract (English)

Abstract
Aim: COVID-19: Range of high-resolution computed tomography chest findings in DMCH, Darbhanga.
Material and Methods: This study was conducted in the department of Radiology, Katihar Medical college and
Hospital, Katihar, Bihar, India for one year. 40 patients aged 22 to 83 years who were tested positive for COVID19 by RT-PCR along with simultaneous conduction of HRCT chest in Radiology Department. These patients
presented in either outpatient or emergency with symptoms of fever, cough/sore throat, or shortness of breath.
PCR negative patients with positive HRCT chest findings for COVID-19 were excluded. All images were taken
on 128 slice CT scanner with patient in supine position and scanning done from lung apices to costo-phrenic
angles. The main scanning parameters were 120 KVP, 450 mAs, pitch 1.4, FOV of 406 mm and slice thickness
of 1mm. CT was done without contrast. Images were sent to workstation and picture archiving and communication
systems (PACS).
Results: The most frequent finding on HRCT chest was combined GGO and consolidation 19(47.5%) with
posterior and sub-pleural distribution 37(92.5%). Bilaterality was seen in 37(92.5%) while one (2.5%) had
unilateral finding. Associated findings were lymphadenopathy 11(27.5%), pleural effusion 6(15.0%),
bronchiectasis 5(12.5%) and emphysema 2(5.0%). Among two patients of early stage (0-2 days), one had normal
HRCT, while other had GGO alone. Consolidation, crazy-paving and vascular enlargement were absent. Of nine
patients of progressive stage (3-5 days), combined GGO and consolidation 4(44.4%) and GGO alone 3(33.3%)
were seen. During late stage (6-12 days), among 29 patients, combined GGO and consolidation 15(51.7%), subpleural lines/bands 14(48.3%), vascular enlargement 13(44.8%) and crazy paving pattern 11(37.9%) were noted.
Conclusion: HRCT chest evolving characteristics are effective in understanding variation in pattern of
coronavirus disease. Identification of imaging patterns with respect to infection time course is an effective
paramount for disease diagnosis, understanding progression and potential complications of disease