To evaluate cyclic changes of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of normal uterus in different age groups during the menstrual cycle, and the correlation with serum female hormone levels.29 normal volunteers accepted diffusion tensor imaging of the uterus on menstrual phase (MP), follicular phase (FP), ovulatory phase (OP) and luteal phase. FA and ADC values of different uterine layers on midsagittal images were measured. Differences between two age groups during the menstrual cycle were evaluated using liner mixed models and one-way analysis of variance. Pearson correlation analysis compared variation of FA and ADC values with serum female hormone levels measured in MP.During menstrual cycle, endometrial FA values declined, whereas ADC values increased with significant differences (p < 0.05). Serum oestradiol (E) levels correlated moderately with variations of FA values between MP-FP (p = 0.045; r = 0.389) and MP-OP (p = 0.008; r = 0.511). FA and ADC values of junctional zones showed no significant difference (p > 0.05) as well as FA values of myometrium (p = 0.0961), while ADC values of myometrium showed significant increase from menstrual phase to luteal phase (p < 0.05). FA and ADC values of uterine three zonal structures showed significant differences (p < 0.05) at each phase during the menstrual cycle. No significant difference of FA and ADC values was found between age groups (p > 0.05).Dynamic changes of uterine FA and ADC values were observed during menstrual cycle. Variation of FA values between MP-FP, MP-OP correlated moderately with serum E levels.No publications on the relationship between FA and ADC values and the female hormone levels were found; our study prospectively investigated the cyclic changes of FA and ADC values of the normal uterus and the correlation with the basic serum female hormone levels in MP.
To improve the infection control and prevention practices against coronavirus disease 2019 (COVID-19) in radiology department through loophole identification and providing rectifying measurements.Retrospective analysis of 2 cases of health-care-associated COVID-19 transmission in 2 radiology departments and comparing the infection control and prevention practices against COVID-19 with the practices of our department, where no COVID-19 transmission has occurred.Several loopholes have been identified in the infection control and prevention practices against COVID-19 of the 2 radiology departments. Loopholes were in large part due to our limited understanding of the highly contagious coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is characterized by features not observed in other SARS viruses. We recommend to set up an isolation zone for handling patients who do not meet the diagnostic criteria of COVID-19 but are not completely cleared of the possibility of infection.Loopholes in the infection control and prevention practices against COVID-19 of the 2 radiology departments are due to poor understanding of the emerging disease which can be fixed by establishing an isolation zone for patients not completely cleared of SARS-CoV-2 infection.
Abstract Objectives To assess the performance of the “dark blood” (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients. Materials and methods Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a “contrast-enhancement-boost” technique. Quantitative parameters of aortic wall image quality were evaluated. Results Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNR outer (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise ( p > 0.99) and increased SNR ( p < 0.001), CNR outer ( p = 0.006), and CNR inner ( p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets. Conclusion DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences. Critical relevance statement Deep-learning-based “dark blood” images improve vessel wall image wall quality and boundary visualization. Key Points Dark blood CTA improves image quality and provides better aortic wall visualization. Deep-learning CTA presented higher quality and subjective scores compared to HIR. Combination of dark blood and deep-learning reconstruction obtained the best overall performance. Graphical Abstract
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