Ribonucleic acid (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 Italian research published online on 1 October 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.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
