Objective To study the value of diffusion tensor imaging(DTI) in early assessment of prognosis of hypoxic-ischemicencephalopathy(HIE) in new infants. Methods 96 cases of full term infants with HIE underwent DTI examinations at 0～12 days and 6～36 months. Based on clinical diagnostic criteria, 96 cases were divided into three groups: mild, moderate and severe HIE groups. Fractional anisotropy(FA) values in the corpus callosum splenium and the posterior limb of internal capsules were measured. Results (1) Varying degrees of FA groups was significantly different from the same site, increased linearly with time,but the magni-tude of change was different;(2)When FA value of corpus callosum splenium in the cut-off point was ≥0.437,the sensitivity(SE) was 83.8% , the specificity(SP) was 18.6% , ROC area under the curve was 0.903 , 95% confidence interval was from 0.846 to 0.961,standard error was 0.029;when FA value of posterior limb of internal capsule in the cut-off point ≥0.391,SE was 86.5%, SP was 11.9% ,ROC area under the curve was 0.940,95% confidence interval was from 0.898 to 0.984,standard error was 0.022. Conclusion MR DTI examination is of significant value in evaluating early the prognosis of HIE in infants.

Objective:To explore the feasibility of 60 kV low-dose scanning technique on chest combined with ClearInfinity deep learning reconstruction algorithm in the diagnosis of pediatric mycoplasma pneumonia.Methods:A total of 132 pediatric patients,who admitted to Taihe Hospital Affiliated to Hubei Medical College and were diagnosed as mycoplasma pneumonia,were selected,and all of them underwent computed tomography(CT)scans on chest.They were randomly divided into routine dose group(66 cases),low dose ClearView and ClearInfinity group(66 cases).In the routine dose group,the tube voltage of CT scan on chest was 100 kV,and 50%ClearView iterative algorithm was adopted in this group.The tube voltage of CT scan on chest was 60kV in low dose ClearView and ClearInfinity group,and 50%ClearView iterative algorithm and 50%ClearInfinity deep learning reconstruction algorithm were used respectively to conduct reconstruction.The difference of radiation dose among the three groups was compared.The CT values and standard deviation(SD)values,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of the region of interest(ROI)of the images of 3 groups were measured and calculated.The images were subjectively evaluated by two diagnostic attending physicians with 10 years of work experience,and the Kappa test was adopted to analyze the consistency of the score results.Results:Compared with the routine dose group,the volume CT dose index(CTDIvol)values,dose-length product(DLP)values and effective radiation dose(ED)values of the low dose ClearView group and ClearInfinity group reduced respectively by 87.58%,87.24%and 88.00%,and the differences were statistically significant(t=4 584.07,63.73,61.27,P<0.01).The noise values of left and right lung of the routine dose group were significantly lower than those of the low dose ClearView group,while were significantly higher than those of ClearInfinity group,and the differences were significant(Z=-9.912,-7.013,P<0.01),and the difference of them between low dose ClearView group and ClearInfinity group was significant(Z=-9.912,P<0.01).The SNR and CNR of left and right lung of low dose ClearView group were significantly lower than those of the routine dose group,with statistically significant(t=-34.810,5.522,P<0.01),while these of the low dose ClearInfinity group were significant higher than them of the routine dose group(t=3.544,-8.674,P<0.05),respectively.The two attending physicians had favorable consistency in the subjective evaluation for images(Kappa>0.75,P<0.01).The subjective score of the routine dose group was not significantly different with that of the low dose ClearInfinity group(P>0.05),but was significantly better than that of the low dose ClearView group(Z=-6.425,P<0.01).Conclusion:For pediatric patients with mycoplasma pneumonia,the 60 kV low dose CT on chest combined with ClearInfinity deep learning reconstruction algorithm can ensure image quality on the premise of reducing radiation,which can ensure the diagnostic effect.