Objective:To explore the application effect of case-based learning (CBL) in teaching magnetic resonance imaging (MRI) for non-imaging clinical professional postgraduates.Methods:Eighty non-imaging clinical professional postgraduates who had standardized residency training from 2017 to 2019 were selected as the participants and were randomly divided into two groups, experimental group and control group. The experimental group adopted CBL, and the control group adopted traditional teaching mode. After the standardized training in the radiology department, the differences in image reading scores, theoretical scores and course evaluation were compared between the two groups. SPSS 25.0 statistical software was used for analysis. Independent t test was used for the measurement data of normal distribution, Mann-Whitney U test was used for the measurement data of skewed distribution, and categorical variables were compared by chi-square test. Results:In the reading scores of MRI, the scores of the experimental group and the control group were (82.53 ± 5.72) points and (77.38 ± 6.14) points respectively, and the number of students in the experimental group whose reading scores were between 80-100 segment was 63.6% higher than that in the control group, with significant differences between the two groups ( P < 0.001), but without significant differences in theoretical average scores between the two groups ( P > 0.05). In addition, in the course evaluation, except for the index of learning burden, there were significant differences in other indexes between the experimental group and the control group ( P < 0.05). Conclusion:In the teaching of MRI, the application of the CBL helps non-imaging clinical professional postgraduates improve their MRI diagnostic thinking and independent reading ability.

Objective:To explore the application value of artificial intelligence (AI) in image post-processing of reconstructed CTA based on CT cerebral perfusion (CTP).Methods:Clinical and radiological data of 100 patients suspected of cerebrovascular diseases in Hebei General Hospital from January to July 2020 were retrospectively selected. All patients were divided into A and B group on average according to the different examination schemes. Cerebral CTP examination was performed in group A (the temporal maximum intensity projective data set generated by the first 5 time phases in the maximum period of the difference between arteriovenous CT values selected as subgroup A1, and the corresponding original thin-layer images selected as subgroup A2), single phase CTA examination was performed in group B, manual and AI image post-processing were performed respectively. Subjective scoring of the image data was performed, and the objective bid evaluation indexes such as CT value, noise (SD), signal-to-noise ratio (SNR), contrast to noise ratio (CNR) were measured, the qualified rate of artificial and AI vascular segmentation was counted, and post-processing time were recorded. The objective evaluation indexes were compared between three groups using one-way ANOVA, and the Kruskal-Wallis H test was used to compare the difference of subjective scores.Results:Statistically significant differences were observed in subjective score and objective evaluation index of original images among group A1, group A2 and group B (all P<0.05). Among them, arterial enhancement, arteriolar detail display score, cerebral artery CT value, SNR and CNR in group A1 were higher than those in group A2 and group B (all P<0.05). In a total of 100 patients with 1 100 blood vessels, the qualified rates of AI vascular segmentation in group A1 [98.4% (541/550)] and group B [98.7% (543/550)] were higher than those of manual [82.9% (456/550), 87.1% (479/550), χ2=77.392, 56.521, P<0.001], but the qualified rate of AI vascular segmentation of group A2 [78.4% (431/550)] was lower than that of manual [85.6% (471/550), χ2=9.855, P=0.002]. The completion time of AI post-processing were reduced by 56.30%, 49.63%, 50.81%, respectively than those with manual. Conclusion:Compared with manual image post-processing, AI has certain advantages in image quality and work efficiency of reconstructed CTA post-processing based on CTP de-noising dataset, and it is worth popularizing and applying in the image post-processing of cerebrovascular disease, combined with artificial quality control.