European radiology
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Comparative Study
Speed of sound ultrasound: comparison with proton density fat fraction assessed with Dixon MRI for fat content quantification of the lower extremity.
To compare speed of sound (SoS) ultrasound (US) of the calves with Dixon magnetic resonance imaging (MRI) for fat content quantification. ⋯ • Correlations of speed of sound with Dixon MRI fat fraction measurements of the same body location were very strong to moderate. • Speed of sound measurements showed a high repeatability. • Speed of sound provides a sufficient discrimination range for fat fraction estimates.
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The study aimed to validate automated quantification of high and low signal intensity volumes using ultrashort echo-time MRI, with CT and pulmonary function test (PFT) as references, to assess the severity of structural alterations in cystic fibrosis (CF). ⋯ Automated quantification of abnormal signal intensity volumes relates to CF severity and allows reproducible cross-sectional and longitudinal assessment.
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To characterize and interpret the CT imaging signs of the 2019 novel coronavirus (COVID-19) pneumonia in China. ⋯ • The CT signs of the COVID-19 pneumonia are mainly distributed in the lobular core, subpleural and diffused bilaterally. • The CT signs include the "parallel pleura sign," "paving stone sign," "halo sign," and "reversed halo sign." • During the follow-up, the distribution of lobular core, the fusion of lesions, and the organization changes at late stage will appear.
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To retrospectively analyze the chest computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia. ⋯ • The predominant CT features of COVID-19 pneumonia are multiple ground-glass opacities with or without consolidation and, with both lungs, multiple lobes and especially the lower lobe affected. • CT plays a crucial role in early diagnosis and assessment of COVID-19 pneumonia progression. • CT findings of COVID-19 pneumonia may not be consistent with the clinical symptoms or the initial RT-PCR test results.
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Multicenter Study
Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.
The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. ⋯ • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.