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Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

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.

Purpose To demonstrate the feasibility of contrast material-enhanced ulrasonographic (US) nephrostograms to assess ureteral patency after percutaneous nephrolithotomy (PCNL) in this proof-of-concept study. Materials and Methods For this HIPAA-compliant, institutional review board-approved prospective blinded pilot study, patients undergoing PCNL provided consent to undergo contrast-enhanced US and fluoroscopic nephrostograms on postoperative day 1. For contrast-enhanced US, 1.5 mL of Optison (GE Healthcare, Oslo, Norway) microbubble contrast agent solution (perflutren protein-type A microspheres) was injected via the nephrostomy tube. Unobstructed antegrade ureteral flow was defined by the presence of contrast material in the bladder. Contrast-enhanced US results were compared against those of fluoroscopic nephrostograms for concordance. Results Ten studies were performed in nine patients (four women, five men). Contrast-enhanced US demonstrated ureteral patency in eight studies and obstruction in two. One patient underwent two studies, one showing obstruction and the second showing patency. Concordance between US and fluoroscopic assessments of ureteral patency was evaluated by using a Clopper-Pearson exact binomial test. These results were perfectly concordant with fluoroscopic nephrostogram results, with a 95% confidence interval of 69.2% and 100%. No complications or adverse events related to contrast-enhanced US occurred. Conclusion Contrast-enhanced US nephrostograms are simple to perform and are capable of demonstrating both patency and obstruction of the ureter. The perfect concordance with fluoroscopic results across 10 studies demonstrated here is not sufficient to establish diagnostic accuracy of this technique, but motivates further, larger scale investigation. If subsequent larger studies confirm these preliminary results, contrast-enhanced US may provide a safer, more convenient way to evaluate ureteral patency than fluoroscopy. © RSNA, 2016 Online supplemental material is available for this article.

PurposeTo compare the diagnostic performance of dual-energy (DE) computed tomography (CT) with two simultaneously administered contrast agents (hereafter, dual contrast) with that of conventional CT in the evaluation of the presence and source of extravasation in penetrating abdominopelvic trauma.Materials and methodsInstitutional animal care and use committee approval was obtained, and the study was performed in accordance with National Institutes of Health guidelines for the care and use of laboratory animals. Five rabbits with bowel trauma, vascular penetrating trauma, or both were imaged with simultaneous iodinated intravenous and bismuth subsalicylate enteric contrast material at DE CT. Four attending radiologists and six radiology residents without prior DE CT experience each evaluated 10 extraluminal collections to identify the vascular and/or enteric origin of extravasation and assess their level of diagnostic confidence, first with virtual monochromatic images simulating conventional CT and then with DE CT material decomposition attenuation maps.ResultsOverall accuracy of identification of source of extravasation increased from 78% with conventional CT to 92% with DE CT (157 of 200 diagnoses vs 184 of 200 diagnoses, respectively; P < .001). Nine radiologists were more accurate with DE CT; one had no change. Mean confidence increased from 67% to 81% with DE CT (P < .001).ConclusionIn a rabbit abdominopelvic trauma model, dual-contrast DE CT significantly increased accuracy and confidence in the diagnosis of vascular versus enteric extravasated contrast material.

PurposeTo evaluate the feasibility of using a commercially available clinical dual-energy computed tomographic (CT) scanner to differentiate the in vivo enhancement due to two simultaneously administered contrast media with complementary x-ray attenuation ratios.Materials and methodsApproval from the institutional animal care and use committee was obtained, and National Institutes of Health guidelines for the care and use of laboratory animals were observed. Dual-energy CT was performed in a set of iodine and tungsten solution phantoms and in a rabbit in which iodinated intravenous and bismuth subsalicylate oral contrast media were administered. In addition, a second rabbit was studied after intravenous administration of iodinated and tungsten cluster contrast media. Images were processed to produce virtual monochromatic images that simulated the appearance of conventional single-energy scans, as well as material decomposition images that separate the attenuation due to each contrast medium.ResultsClear separation of each of the contrast media pairs was seen in the phantom and in both in vivo animal models. Separation of bowel lumen from vascular contrast medium allowed visualization of bowel wall enhancement that was obscured by intraluminal bowel contrast medium on conventional CT scans. Separation of two vascular contrast media in different vascular phases enabled acquisition of a perfectly coregistered CT angiogram and venous phase-enhanced CT scan simultaneously in a single examination.ConclusionCommercially available clinical dual-energy CT scanners can help differentiate the enhancement of selected pairs of complementary contrast media in vivo.