Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) plays an essential role in the evaluation of COVID-19. This retrospective study aims to determine and compare the pulmonary changes in Iraqi patients with COVID-19 disease across the first two weeks after onset of symptoms using computerized tomography (CT) scan. Ninety-six patients with COVID-19 disease were enrolled in this study. Patients were divided into two groups according to the duration of symptoms (the first group has been scanned within the first week of presentation while the second group has been scanned in the second week). Results: The CT findings in the first and second group were as follows: ground glass opacity (GGO) were 94.3% vs. 88.5%, consolidation were 25.7% vs. 34.6%, broncho vascular thickening were 18.6% vs. 7.7%, crazy paving appearance were 15.7% vs. 3.8%, tree-in-bud appearance were 4.3% vs. 10.7%, pulmonary nodules were 5.1% vs. 7.7%, and bronchiectasis were 5.5% vs. 7.7%. Pleural effusion and cavitation were seen only in the first group (2.9% and 1.4% respectively). The distribution of CT changes across the two groups were as follows: bilateral changes were 85.7% vs. 100%; central distribution were 11.4% vs. 11.5%; peripheral distribution were 64.3% vs. 42.3%, and diffuse (central and peripheral) distribution were 24.3% vs. 46.2% while multilobar distribution were 70% vs. 80.8%. Conclusion: The type, extent, and distributions of pulmonary manifestations associated with COVID-19 infection are significantly different between the two groups who have been scanned in different stages of the disease.

In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Datasets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint’s production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intra-reader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint’s ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms.

Background: COVID-19 has emerged recently and become of global concern. Computed tomography (CT) plays a vital role in the diagnosis. Objectives: To characterize the pulmonary CT changes and distributions of COVID-19 infection in regard to different age groups. Methods: Chest CT scan of 104 symptomatic patients with COVID-19 infection, from 7 Iraqi isolation centers were retrospectively analyzed between March 10th and April 5th, 2020. Patients were sub-classified according to their ages to three groups (young adult:20-39years, middle age:40-59years and old age:60- 90years). Results: The most common findings were ground-glass opacities (GGO) (92.3%, followed by consolidation (27.9%), bronchovascular thickening (15.4%), and crazy-paving (12.5%). Less commonly, there were tree-inbud (6.7%), pulmonary nodules (5.8%), bronchiectasis (3.8%), pleural effusion (1.9%), and cavitation (1%). There were no hallo sign, reversed hallo sign, nor mediastinal lymphadenopathy. Pulmonary changes were unilateral in 16.7% and bilateral in 83.3%, central in 14.6%, peripheral in 57.3%, and diffuse (central and peripheral) in 28.1%. Most cases showed multi-lobar changes (70.8%), while the lower lobe was more commonly involved (17.7%) than middle lobe/lingula (8.3%) and upper lobe (3.1%). In unilateral involvement, changes were more on the right (68.8%) than left (31.2%) side. Compared with middle and old age groups, young adult patients showed significantly lesser frequency of consolidation (17% vs. 13.3% and 37%), diffuse changes 28.1% (14.2% vs. 35.3% and 40.5%), bilateral disease (71.4% vs. 94.1% and 85.2%), and multi-lobar involvement (51.4% vs. 82.4% and 81.4%) respectively. Conclusion: Bilateral and peripheral GGO were the most frequent findings with the right side and lower lobar predilection. Extent and pattern seem to be age-related.

No abstract