AJR. American journal of roentgenology
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AJR Am J Roentgenol · Dec 2020
Rapid Deployment of Home PACS Workstations to Enable Social Distancing in the Coronavirus Disease (COVID-19) Era.
OBJECTIVE. Social distancing is considered an effective mitigation strategy for coronavirus disease (COVID-19), and remote interpretation of radiologic studies is one approach to social distancing within the radiology department. ⋯ CONCLUSION. Transitioning from on-site interpretation to remote interpretation requires a careful balancing of hospital and departmental finances, engineering choices, and educational and philosophical workflow issues.
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AJR Am J Roentgenol · May 2020
Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome.
OBJECTIVE. Since the outbreak of the novel coronavirus pulmonary illness coronavirus disease 2019 (COVID-19) in China, more than 79,000 people have contracted the virus worldwide. The virus is rapidly spreading with human-to-human transmission despite imposed precautions. ⋯ CONCLUSION. The review of experiences with the MERS and SARS outbreaks will help us better understand the role of the radiologist in combating the outbreak of COVID-19. The known imaging manifestations of the novel coronavirus and the possible unknowns will also be discussed.
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AJR Am J Roentgenol · Aug 2021
Prospective PI-RADS v2.1 Atypical Benign Prostatic Hyperplasia Nodules With Marked Restricted Diffusion: Detection of Clinically Significant Prostate Cancer on Multiparametric MRI.
BACKGROUND. On the basis of expert consensus, PI-RADS version 2.1 (v2.1) introduced the transition zone (TZ) atypical benign prostatic hyperplasia (BPH) nodule, defined as a TZ lesion with an incomplete or absent capsule (T2 score, 2). PI-RADS v2.1 also included a revised scoring pathway whereby such nodules, if exhibiting marked restricted diffusion (DWI score, 4-5), are upgraded from overall PI-RADS category 2 to category 3 (2 + 1 TZ lesions). ⋯ The rate of csPCa in atypical BPH nodules with marked restricted diffusion was low (6%) and not significantly different from that of conventional 3 + 0 TZ lesions (11%). CLINICAL IMPACT. The results provide prospective clinical data about the revised TZ scoring criterion and pathway in PI-RADS v2.1 for atypical BPH nodules with marked restricted diffusion.
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AJR Am J Roentgenol · Dec 2020
Comparative StudySplit-Bolus, Single-Acquisition, Dual-Phase Abdominopelvic CT Angiography for the Evaluation of Lung Transplant Candidates: Image Quality and Resource Utilization.
OBJECTIVE. The purpose of this study was to assess the image quality and resource utilization of single-injection, split-bolus, dual-enhancement abdominopelvic CT angiography (hereafter referred to as dual-enhancement CTA) performed for combined vascular and solid organ assessment compared with those of single-injection, single-enhancement abdominopelvic CT angiography (hereafter referred to as single-enhancement CTA) for vascular assessment in combination with additional examinations (CT, MRI, and US) performed to assess for malignancy in lung transplant candidates. MATERIALS AND METHODS. ⋯ Cohort B underwent 44 additional imaging studies (mean effective dose, 12.7 ± 6.5 mSv) at a total cost of $12,846 per patient (resulting in a 30.6% reduction in cost for dual-enhancement CTA studies; p < 0.0001). CONCLUSION. Dual-enhancement abdominopelvic CTA allows combined vascular and abdominopelvic solid organ assessment with improved image quality and a lower cost compared with traditional imaging pathways.
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AJR Am J Roentgenol · Jan 2021
A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.
Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI. ⋯ A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.