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To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction.In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities.In contrast to influenza patients, lung involvement remains high in COVID-19 patients 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19.This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages. A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDIvol) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists. MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%; P > 0.37). Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients. • MBIR improved quantitative image quality but not lesion detection compared with FBP. • Increased CNR by low tube voltages did not improve lesion detection. • Changes in image noise and CNR do not directly influence diagnostic accuracy.