
RNA from the SARS-CoV-2 virus was found among internal components of a CT scanner that had performed a high volume of exams on COVID-19 patients, according to research published online on October 1 in European Radiology Experimental. In good news, the RNA was discovered only in the inward airflow filter.
"These results are encouraging since this filter may act as a partial barrier to [dissemination of] the virus," wrote the researchers led by Dr. João Matos of the University of Genoa in Genova, Italy.
Seeking to determine if the internal gantry components of their 16-slice CT scanner (LightSpeed, GE Healthcare) could be contaminated after scanning 180 consecutive patients with COVID-19 over a 26-day period, the researchers opened the CT gantry and sampled the following eight components: gantry case, inward airflow filter, gantry motor, x-ray tube, outflow fan, fan grid, detectors, and the x-ray tube filter.
All samples were then analyzed using reverse transcription polymerase chain reaction (RT-PCR) to detect SARS-CoV-2 RNA. The researchers also evaluated the samples for the presence of bacterial and fungal agents.
With the exception of the inward airflow filter, all internal CT gantry components were devoid of SARS-CoV-2 RNA, according to the researchers. They also did not find any bacterial or fungal agents.
"Even after 26 days of intensive use, conventional sanitization measures were most probably successful in preventing large-scale contamination of the CT scanner," the authors wrote.



![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=100&q=70&w=100)






![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)








