Objective Exploration of the application value of the third-generation dual source CT low tube voltage (70 kVp) scanning combined with advanced modeling iterative reconstruction (ADMIRE) technique in children with adenoid examination. Methods CT scans were performed in patients with clinically suspected adenoid hypertrophy. They were divided into two groups according to the time of treatment. Group A (40 cases) : low tube voltage (70 kVp) scan, reference tube current 163 mAs, reconstruction with ADMIER, Intensity 3; Group B (40 cases) : conventional 100 kVp, reference tube current 163 mAs, conventional (filtered back-projection, FBP) reconstruction;rest of the scanning parameters remained unchanged. The subjective scores and objective quality indicators of the images (CT value, image noise, signal noise ratio (SNR) , contrast noise ratio (CNR)) and radiation dose of the two groups were compared. Results The difference of radiation dose between group A and group B was statistically significant (P < 0.05). The radiation dose of group A was lower than that of group B by 77.58％.Compared with group B, the image noise of group A increased by 0.002％; the SNR decreased by 0.01％; CNR increased by 0.03％; there was no significant difference in objective quality evaluation index and subjective score between two groups in the image quality (P> 0.05). Conclusion The third-generation dual-source CT low-tube voltage (70 kVp) combined with ADMIRE reconstruction technique for children with adenoid scan can effectively reduce the radiation dose while ensuring image quality.

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.