Detection of Unsuspected Coronavirus Disease 2019 Cases by Computed Tomography and Retrospective Implementation of the Radiological Society of North America/Society of Thoracic Radiology/American College of Radiology Consensus Guidelines Purpose: The purpose of this article was to report the utility of computed tomography (CT) for detecting unsuspected cases of Coronavirus disease 2019 (COVID-19) and the utility of the Radiological Society of North America (RSNA)/Society of Thoracic Radiology (STR)/American College of Radiology (ACR) consensus guidelines for COVID-19 reporting. Materials and Methods: A total of 22 patients of the 156 reverse transcriptase polymerase chain reaction confirmed COVID-19 patients who were hospitalized between March 27, 2020 and March 31, 2020 at our quaternary care academic medical center and who underwent CT imaging within 1 week of admission were included in this retrospective study. Demographics and clinical data were extracted from the electronic medical record system. Two thoracic radiologists independently categorized each CT study on the basis of RSNA/STR/ACR consensus guidelines. Disagreement in categorization was resolved by consensus discussion with a third thoracic radiologist. Results: At the time of imaging, 16 patients (73%) were suspected of COVID-19, and 6 patients (27%) were not. Common symptoms at presentation were fever (73%), cough (77%), and gastrointestinal symptoms (59%). An overall 63% of suspected COVID-19 patients exhibited shortness of breath, whereas 0 unsuspected COVID-19 patients did (P=0.02). On the basis of the RSNA consensus guidelines, 68%, 18%, 9%, and 5% of studies were categorized as “typical appearance,” “indeterminate appearance,” “atypical appearance,” and “negative for pneumonia,” respectively. There was no difference of category distribution between suspected and unsuspected COVID-19 patients (P=0.20), with “typical appearance” being the most prevalent in both (69% vs. 67%, respectively). Conclusions: It is important to recognize imaging features of COVID-19 pneumonia even in unsuspected patients. Implementation of the RSNA/STR/ACR consensus guidelines may increase consistency of reporting and convey the level of suspicion for COVID-19 to other health care providers, with “typical appearance” especially warranting further attention. Dr B.P.L. is a textbook author and editor for Elsevier and receives royalties for his prior work. Dr E.J.F. reports grant funding from the American College of Radiology Innovation Fund and the National Cancer Institute Research Diversity Supplement for work not related to this manuscript. The remaining authors declare no conflicts of interest. Correspondence to: Brent P. Little, MD, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114 (e-mail: blittle@partners.org). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Prevalence of Burnout Among Cardiothoracic Radiologists Purpose: Burnout is a psychological syndrome in response to chronic occupational stressors. The prevalence of burnout among medical professionals has been increasing, and recent studies have shown that radiologists are among those affected. We investigated the prevalence of burnout and assessed associated factors among cardiothoracic radiologists. Materials and Methods: Society of Thoracic Radiology members were invited to complete an anonymous cross-sectional survey that included an adapted Maslach Burnout Inventory and questions about demographics, work place characteristics, and stressors. Results: The survey response rate was 33.1% (290/874). Per-item response rate ranged from 94% to 100% with a median of 99%. The prevalence of emotional exhaustion was 66.8% (186/283), depersonalization was 79% (223/283), and low personal accomplishment was 23% (65/280). There were no statistically significant differences between academic and private practice. There was a trend toward worse burnout in women, but this was not statistically significant. Being in early career (0 to 10 y since fellowship) was associated with low personal accomplishment [odds ratio (OR): 2.07, 95% confidence interval (CI): 1.08-3.99]. Those working fewer than 51 hours per week were significantly less likely to report emotional exhaustion (OR: 0.55, 95% CI: 0.33-0.90). The odds of emotional exhaustion for those producing fewer than 7500 work relative value units per year were approximately half of those exceeding that number (OR: 0.46, 95% CI: 0.22-0.95). Conclusions: The prevalence of burnout among cardiothoracic radiologists is comparable to that reported for radiologists in other subspecialties such as musculoskeletal and interventional radiology. High work relative value unit productivity and longer work hours are associated with higher prevalence of burnout. The authors declare no conflicts of interest Correspondence to: Ronald L. Eisenberg, MD, Department of Radiology, Harvard University School of Medicine, Boston, MA 02215 (e-mail: rleisenb@bidmc.harvard.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study Objective: This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection. Methods: The clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography (CT) scans were extracted and dimensionality reduction was performed. Support vector machines were used to construct radiomics signatures and to compare differences between the 2 groups. We also compared the differences of signature scores in the clinical, laboratory, and CT image feature subgroups and finally analyzed the correlation between the radiomics features of the constructed signature and the other features including clinical, laboratory, and CT imaging features. Results: The signature has a good classification effect for the stable group and the progressive group, with area under curve, sensitivity, and specificity of 0.833, 80.95%, and 74.42%, respectively. Signature score differences in laboratory and CT imaging features between subgroups were not statistically significant (P>0.05); cough was negatively correlated with GLCM Entropy_angle 90_offset4 (r=−0.578), but was positively correlated with ShortRunEmphhasis_AllDirect_offset4_SD (r=0.454); C-reactive protein was positively correlated with Cluster Prominence_ AllDirect_offset 4_ SD (r=0.47). Conclusion: The radiomics signature of the whole lung based on machine learning may reveal the changes of lung microstructure in the early stage and help to indicate the progression of the disease. The authors declare no conflicts of interest. Correspondence to: Zhenyu Shu, MD, NO.158 Shangtang Road, Hangzhou City, Zhejiang Province 310014, China (e-mail: cooljuty@hotmail.com). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language The diagnosis of coronavirus disease 2019 (COVID-19) is confirmed by reverse transcription polymerase chain reaction. The utility of chest radiography (CXR) remains an evolving topic of discussion. Current reports of CXR findings related to COVID-19 contain varied terminology as well as various assessments of its sensitivity and specificity. This can lead to a misunderstanding of CXR reports and makes comparison between examinations and research studies challenging. With this need for consistency, we propose language for standardized CXR reporting and severity assessment of persons under investigation for having COVID-19, patients with a confirmed diagnosis of COVID-19, and patients who may have radiographic findings typical or suggestive of COVID-19 when the diagnosis is not suspected clinically. We recommend contacting the referring providers to discuss the likelihood of viral infection when typical or indeterminate features of COVID-19 pneumonia on CXR are present as an incidental finding. In addition, we summarize the currently available literature related to the use of CXR for COVID-19 and discuss the evolving techniques of obtaining CXR in COVID-19-positive patients. The recently published expert consensus statement on reporting chest computed tomography findings related to COVID-19, endorsed by the Radiological Society of North American (RSNA), the Society of Thoracic Radiology (STR), and American College of Radiology (ACR), serves as the framework for our proposal. The authors declare no conflicts of interest. Correspondence to: Diana E. Litmanovich, MD, Department of Cardiothoracic Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215 (e-mail: dlitmano@bidmc.harvard.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Cardiac Magnetic Resonance Imaging Feature Tracking Demonstrates Altered Biventricular Strain in Obese Subjects in the Absence of Clinically Apparent Cardiovascular Disease No abstract available

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed. I-ELCAP Investigators: Mount Sinai School of Medicine, New York, NY: Claudia I. Henschke, Principal Investigator, David F. Yankelevitz, Rowena Yip, Artit Jirapatnakul, Raja Flores, Andrea Wolf; Weill Cornell Medical College: Dorothy I. McCauley, Mildred Chen, Daniel M. Libby, James P. Smith, Mark Pasmantier; Cornell University: A. P. Reeves; CBNS, City University of New York at Queens College, Queens, NY; Steven Markowitz, Albert Miller; Fundacion Instituto Valenciano de Oncologia, Valencia, Spain: Jose Cervera Deval; University of Toronto, Princess Margaret Hospital, Toronto, ON, Canada: Heidi Roberts, Demetris Patsios; Azumi General Hospital, Nagano, Japan: Shusuke Sone, Takaomi Hanaoka; Clinica Universitaria de Navarra, Pamplona, Spain: Javier Zulueta, Juan P. de-Torres, Maria D. Lozano; Swedish Medical Center, Seattle, WA: Ralph Aye, Kristin Manning; Christiana Care, Helen F. Graham Cancer Center, Newark, DE: Thomas Bauer; National Cancer Institute Regina Elena, Rome, Italy: Stefano Canitano, Salvatore Giunta; St.Agnes Cancer Center, Baltimore, MD: Enser Cole; LungenZentrum Hirslanden, Zurich, Switzerland: Karl Klingler; Columbia University Medical Center, New York, NY: John H.M. Austin, Gregory D. N. Pearson; Hadassah Medical Organization, Jerusalem, Israel: Dorith Shaham; Holy Cross Hospital Cancer Institute, Silver Spring, MD: Cheryl Aylesworth; Nebraska Methodist Hospital, Omaha, NE: Patrick Meyers; South Nassau Communities Hospital, Long Island, NY: Shahriyour Andaz; Eisenhower Lucy Curci Cancer Center, Rancho Mirage, CA; Davood Vafai; New York University Medical Center, New York, NY: David Naidich, Georgeann McGuinness; Dorothy E. Schneider Cancer Center, Mills-Peninsula Health Services, San Mateo, CA: Barry Sheppard; State University of New York at Stony Brook, Stony Brook, NY: Matthew Rifkin; ProHealth Care Regional Cancer Center, Waukesha & Oconomowoc Memorial Hospitals, Oconomowoc, WI: M. Kristin Thorsen, Richard Hansen; Maimonides Medical Center, Brooklyn, NY: Samuel Kopel; Wellstar Health System, Marietta, GA: William Mayfield; St. Joseph Health Center, St. Charles, MO: Dan Luedke; Roswell Park Cancer Institute, Buffalo, NY: Donald Klippenstein, Alan Litwin, Peter A. Loud; Upstate Medical Center, Syracuse, NY: Leslie J. Kohman, Ernest M. Scalzetti; Jackson Memorial Hospital, University of Miami, Miami, FL; Richard Thurer, Nestor Villamizar; State University of New York, North Shore-Long Island Jewish Health System, New Hyde Park, NY: Arfa Khan, Rakesh Shah; The 5th Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China: Xueguo Liu; Mercy Medical Center, Rockville Center, NY: Gary Herzog; Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan: Diana Yeh; National Cancer Institute of China, Beijing, China: Ning Wu; Staten Island University Hospital, Staten Island, NY: Joseph Lowry, Mary Salvatore; Central Main Medical Center: Carmine Frumiento; Mount Sinai School of Medicine, New York, NY: David S. Mendelson; Georgia Institute for Lung Cancer Research, Atlanta, GA: Michael V. Smith; The Valley Hospital Cancer Center, Paramus NJ: Robert Korst; Health Group Physimed/McGill University, Montreal, CA: Jana Taylor; Memorial Sloan-Kettering Cancer Center, New York, NY: Michelle S. Ginsberg; John Muir Cancer Institute, Concord, CA: Michaela Straznicka; Atlantic Health Morristown Memorial Hospital, Morristown, NJ: Mark Widmann; Alta Bates Summit Medical Center, Berkeley, CA: Gary Cecchi; New York Medical College, Valhalla, NY: Terence A.S. Matalon; St. Joseph’s Hospital, Atlanta, GA: Paul Scheinberg; Mount Sinai Comprehensive Cancer Center, Miami Beach, FL: Shari-Lynn Odzer; Aurora St. Luke’s Medical Center, Milwaukee, WI: David Olsen; City of Hope National Medical Center, Duarte, CA: Fred Grannis, Arnold Rotter; Evanston Northwestern Healthcare Medical Group, Evanston, IL: Daniel Ray; Greenwich Hospital, Greenwich, CT: David Mullen; Our Lady of Mercy Medical Center, Bronx, NY: Peter H. Wiernik; Baylor University Medical Center, Dallas, TX: Edson H. Cheung; Sequoia Hospital, Redwood City, CA: Melissa Lim; Glens Falls Hospital, Glens Falls, NY: Louis DeCunzo; Atlantic Medical Imaging, Atlantic City, NJ: Robert Glassberg; Karmanos Cancer Institute, Detroit, MI: Harvey Pass, Carmen Endress; Rush University, Chicago, IL: Mark Yoder, Palmi Shah; Building Trades, Oak Ridge, TN: Laura Welch; Sharp Memorial Hospital, San Diego, CA: Michael Kalafer; Newark Beth Israel Medical Center, Newark, NJ: Jeremy Green; Guthrie Cancer Center, Sayre, PA: James Walsh, David Bertsch; Comprehensive Cancer Centers of the Desert, Palm Springs, CA: Elmer Camacho; Dickstein Cancer Treatment Center, White Plains Hospital, White Plains, NY: Cynthia Chin; Presbyterian Healthcare, Charlotte, NC: James O’Brien; University of Toledo, Toledo, OH: James C. Willey. Dr D.F.Y. is a named inventor on a number of patents and patent applications relating to the evaluation of diseases of the chest including measurement of nodules. Some of these, which are owned by Cornell Research Foundation (CRF), are nonexclusively licensed to General Electric. As an inventor of these patents, Dr. D.F.Y. is entitled to a share of any compensation that CRF may receive from its commercialization of these patents. He is also an equity owner in Accumetra, a privately held technology company committed to improving the science and practice of image-based decision making. Dr D.F.Y. also serves on the advisory board of GRAIL. Dr C.I.H. is the President and serves on the board of the Early Diagnosis and Treatment Research Foundation. She receives no compensation from the Foundation. The Foundation is established to provide grants for projects, conferences, and public databases for research on early diagnosis and treatment of diseases. Dr C.I.H. is also a named inventor on a number of patents and patent applications relating to the evaluation of pulmonary nodules on CT scans of the chest, which are owned by Cornell Research Foundation (CRF). Since 2009, Dr C.I.H. does not accept any financial benefit from these patents including royalties and any other proceeds related to the patents or patent applications owned by CRF. The remaining authors declare no conflicts of interest. Correspondence to: Claudia I. Henschke, PhD, MD, Department of Radiology, P.O. Box 1234, Icahn School of Medicine at Mount Sinai, 1 Gustave Levy Place New York, NY 10029 (e-mail: claudia.henschke@mountsinai.org). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Four-dimensional Flow Magnetic Resonance Imaging Quantification of Blood Flow in Bicuspid Aortic Valve Background: Four-dimensional (D) flow magnetic resonance imaging (MRI) is limited by time-consuming and nonstandardized data analysis. We aimed to test the efficiency and interobserver reproducibility of a dedicated 4D flow MRI analysis workflow. Materials and Methods: Thirty retrospectively identified patients with bicuspid aortic valve (BAV, age=47.8±11.8 y, 9 male) and 30 healthy controls (age=48.8±12.5 y, 21 male) underwent Aortic 4D flow MRI using 1.5 and 3 T MRI systems. Two independent readers performed 4D flow analysis on a dedicated workstation including preprocessing, aorta segmentation, and placement of four 2D planes throughout the aorta for quantification of net flow, peak velocity, and regurgitant fraction. 3D flow visualization using streamlines was used to grade aortic valve outflow jets and extent of helical flow. Results: 4D flow analysis workflow time for both observers: 5.0±1.4 minutes per case (range=3 to 10 min). Valve outflow jets and flow derangement was visible in all 30 BAV patients (both observers). Net flow, peak velocity, and regurgitant fraction was significantly elevated in BAV patients compared with controls except for regurgitant fraction in plane 4 (91.1±29.7 vs. 62.6±19.6 mL/s, 37.1% difference; 121.7±49.7 vs. 90.9±26.4 cm/s, 28.9% difference; 9.3±10.1% vs. 2.0±3.4%, 128.0% difference, respectively; P<0.001). Excellent intraclass correlation coefficient agreement for net flow: 0.979, peak velocity: 0.931, and regurgitant fraction: 0.928. Conclusion: Our study demonstrates the potential of an efficient data analysis workflow to perform standardized 4D flow MRI processing in under 10 minutes and with good-to-excellent reproducibility for flow and velocity quantification in the thoracic aorta. D.Z.G. and M.A.A. contributed equally. The authors declare no conflict of interest. Correspondence to: Muhannad A. Abbasi, MD, Department of Radiology, Northwestern University, 737 North Michigan Avenue, Suite 1600, Chicago, IL 60611 (e-mail: muhannadaboudabbasi@gmail.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

A Dose Simulation X-Ray Software: An Innovating Tool to Reduce Chest Radiograph Exposure in Children Purpose: Chest radiography is one of the most frequent x-ray examinations performed on children. Reducing the delivered dose is always a major task. The objective of our study was to determine the minimum dose to be delivered while maintaining the image quality of chest radiographs, using dose reduction simulation software. Materials and Methods: We included 60 children who had had a chest radiography in 5 groups established according to the diagnostic reference levels equitably represented by weight ranges. The software simulated for each radiograph 6 additional simulated photonic noise images corresponding to 100%, 80%, 64%, 50%, 40%, and 32% of the initial dose. The 360 radiographs were blindly scored by 2 radiologists, according to the 7 European quality criteria and a subjective criterion of interpretability, using a semiquantitative visual Lickert scale. Results: There was no significant difference in scoring between the reference radiograph (100%) and simulated radiographs at 80% of the dose in children between 5 and 20 kg, 50% of the dose in children between 20 and 30 kg, and between simulated radiographs at 40% of the dose in children over 30 kg. Interobserver reproducibility was moderate to excellent. Conclusion: Chest radiography dose might be reduced by 20% in children between 5 and 20 kg, 50% in children between 20 and 30 kg, and 60% in children over 30 kg, without any difference in the image quality appreciation. Software that produced simulated x-ray with decreasing delivered dose is an innovating tool for an optimization process. The local institutional review board approved this single multicentric prospective cohort study (2019-75). The authors declare no conflicts of interest. Correspondence to: Baptiste Morel, PhD, Department of Pediatric Radiology, Clocheville Hospital, 49 Boulevard Beranger, Tours 37000, France (e-mail: bamorel@univ-tours.fr). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Three-dimensional Ultrashort Echotime Magnetic Resonance Imaging for Combined Morphologic and Ventilation Imaging in Pediatric Patients With Pulmonary Disease Purpose: Ultrashort echotime (UTE) sequences aim to improve the signal yield in pulmonary magnetic resonance imaging (MRI). We demonstrate the initial results of spiral 3-dimensional (3D) UTE-MRI for combined morphologic and functional imaging in pediatric patients. Methods: Seven pediatric patients with pulmonary abnormalities were included in this observational, prospective, single-center study, with the patients having the following conditions: cystic fibrosis (CF) with middle lobe atelectasis, CF with allergic bronchopulmonary aspergillosis, primary ciliary dyskinesia, air trapping, congenital lobar overinflation, congenital pulmonary airway malformation, and pulmonary hamartoma. Patients were scanned during breath-hold in 5 breathing states on a 3-Tesla system using a prototypical 3D stack-of-spirals UTE sequence. Ventilation maps and signal intensity maps were calculated. Morphologic images, ventilation-weighted maps, and signal intensity maps of the lungs of each patient were assessed intraindividually and compared with reference examinations. Results: With a scan time of ∼15 seconds per breathing state, 3D UTE-MRI allowed for sufficient imaging of both “plus” pathologies (atelectasis, inflammatory consolidation, and pulmonary hamartoma) and “minus” pathologies (congenital lobar overinflation, congenital pulmonary airway malformation, and air trapping). Color-coded maps of normalized signal intensity and ventilation increased diagnostic confidence, particularly with regard to “minus” pathologies. UTE-MRI detected new atelectasis in an asymptomatic CF patient, allowing for rapid and successful therapy initiation, and it was able to reproduce atelectasis and hamartoma known from multidetector computed tomography and to monitor a patient with allergic bronchopulmonary aspergillosis. Conclusion: 3D UTE-MRI using a stack-of-spirals trajectory enables combined morphologic and functional imaging of the lungs within ~115 second acquisition time and might be suitable for monitoring a wide spectrum of pulmonary diseases. The project underlying this report was funded by the Deutsche Forschungsgemeinschaft (DFG), project number VE1008/1-1 and KO2938/5-1. The Department of Radiology receives a research grant from Siemens Healthcare GmbH. The grant is not specifically directed toward any of the authors. T.B. is an employee of Siemens Healthcare GmbH. The remaining authors declare no conflicts of interest. Correspondence to: Simon Veldhoen, MD, Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, Würzburg 97080, Germany (e-mail: veldhoen_s@ukw.de). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

Computed Tomography Appearance of Unusual Nonaortic Thoracic Aneurysms and Pseudoaneurysms Although aneurysms of the thoracic aorta are easily recognized on computed tomography (CT), nonaortic intrathoracic aneurysms and pseudoaneurysms are unusual and not often encountered by radiologists. These lesions can result in complications such as hemorrhage or symptoms from mass effect. In some cases, patients may be asymptomatic and the aneurysms may represent incidental findings. Radiologists should be familiar with the CT appearances of these rare vascular abnormalities to enable prompt diagnosis. The goals of this pictorial essay are to: (1) illustrate and describe the CT appearances of various unusual intrathoracic nonaortic aneurysms and pseudoaneurysms; (2) discuss the etiology and clinical significance of these lesions; and (3) discuss management options where appropriate. The authors declare no conflict of interest. Correspondence to: Pierre D. Maldjian, MD, Department of Radiology, Rutgers New Jersey Medical School, 185 South Orange Avenue, MSB F-506B, Newark, NJ 07103 (e-mail: maldjipd@njms.rutgers.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved