Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study.

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. Data sets 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 intrareader 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. Classification: CT lung phantoms, reader study.

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging.

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

The RSNA International COVID-19 Open Radiology Database (RICORD).

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.

A 53-Year-Old Man Presents to the ED With Shortness of Breath, Cough, and Fever.

CASE PRESENTATION: A 53-year-old man presented to the ED at a time of low severe acute respiratory syndrome coronavirus 2, also known as coronavirus disease 2019 (COVID-19), prevalence and reported 2 weeks of progressive shortness of breath, dry cough, headache, myalgias, diarrhea, and recurrent low-grade fevers to 39°C for 1 week with several days of recorded peripheral capillary oxygen saturation of 80% to 90% (room air) on home pulse oximeter. Five days earlier, he had visited an urgent care center where a routine respiratory viral panel was reportedly negative. A COVID-19 reverse transcriptase polymerase chain reaction test result was pending at the time of ED visit. He reported a past medical history of gastroesophageal reflux disease that was treated with famotidine. Travel history included an out-of-state trip 3 weeks earlier, but no recent international travel.

The Role of Imaging in the Management of Suspected or Known COVID-19 Pneumonia. A Multidisciplinary Perspective.

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus that has rapidly escalated into a global pandemic leading to an urgent medical effort to better characterize this disease biologically, clinically, and by imaging. In this review, we present the current approach to imaging of COVID-19 pneumonia. We focus on the appropriate use of thoracic imaging modalities to guide clinical management. We also describe radiologic findings that are considered typical, atypical, and generally not compatible with COVID-19. Furthermore, we review imaging examples of COVID-19 imaging mimics, such as organizing pneumonia, eosinophilic pneumonia, and other viral infections.

The Role of Imaging in the Management of Suspected or Known COVID-19 Pneumonia: A Multidisciplinary Perspective.

COVID-19 is an illness caused by a novel coronavirus that has rapidly escalated into a global pandemic leading to an urgent medical effort to better characterize this disease biologically, clinically and by imaging. In this review, we present the current approach to imaging of COVID-19 pneumonia. We focus on the appropriate utilization of thoracic imaging modalities to guide clinical management. We will also describe radiologic findings that are considered typical, atypical and generally not compatible with of COVID-19 infection. Further, we review imaging examples of COVID-19 imaging mimics, such as organizing pneumonia, eosinophilic pneumonia and other viral infections.