Objective To explore the clinical application value of second-generation dual-source CT combined with intelligent modulation and iterative reconstruction in emergency aortic dissection imaging.Methods A total of 40 emergency patients with clinical suspected aortic dissection were included in this study.Conventional scanning was performed in the control group,and large-pitch intelligent modulation and iterative reconstruction were performed in the test group.The mean CT value,mean noise,signal noise ratio(SNR),contrast noise ratio(CNR),effective dose,image quality and aortic root image quality were evaluated and analyzed.Results Totally 40 patients successfully completed CT aortic dissection imaging.There was no difference in image quality between the two groups (P＞ 0.05).The quality of aortic root images in the test group was better than that in the control group,and the difference was statistically significant(x2=22.556,P＜0.05).The mean CT value and mean noise of aorta in the control group were slightly higher than those in the test group.However,SNR and CNR in the test group were higher than those in the control group,and the difference was statistically significant (t =-21.042,-15.924,8.530,11.495,P＜0.05).The effective dose of the control group [(10.59±3.89)mSv] was significantly higher than that [(6.39±0.81) mSv] of the test group,the difference was statistically significant (t =-12.327,P＜0.05).Conclusions The combined intelligent modulation technique and iterative reconstruction technique with dual-source CT large pitch scanning can meet the requirements of image quality and reduce the effective dose,and can be used as a conventional imaging method for emergency CT of aortic dissection.

Objective: To develop the Adolescent Mental Health Literacy Assessment Questionnaire (AMHLAQ), and to evaluate its reliability and validity among undergraduates. Methods: On the basis of the definition of mental health literacy (MHL) and the Knowledge, Attitudes and Practices (KAP) theory, this study constructed a total of 36 items consisting of four dimensions, and scores were measured according to a five point Likert type scale. Using a cluster sampling method, a questionnaire survey was conducted among 3 826 freshmen and sophomore students from two medical schools in Anhui Province. The items were screened by performing t tests, Pearson s correlation coefficient analysis and factor analysis. The reliability and validity of the questionnaire were evaluated using indicators including homogeneity reliability, the split half reliability coefficient, and construct validity. Results: Factor analysis revealed that the AMHLAQ consisted of 22 questions grouped into four domains. The variance cumulative contribution rate was 62.213%. The reliability result showed that the Cronbach s alpha coefficient of the total questionnaire was 0.897, the split half reliability was 0.800, the Cronbach s coefficient of each dimension was 0.796 to 0.885, the split half reliability of each dimension was 0.725 to 0.846, and the indicators had a high level of reliability. Confirmatory factor analysis showed that the model fit was good ( χ 2/df =19.319, P <0.01; RMSEA=0.069). Conclusion AMHLAQ is consistent with the evaluation standard of psychometrics, has good reliability and validity, and can be used to estimate the level of MHL among undergraduates.

Objective:To explore the impact of artificial intelligence compressed sensing technology (CS-AI) on image quality in three-dimensional proton density weighted imaging (3D PDWI) of the unilateral hip joint.Methods:High-resolution unilateral hip imaging was conducted on 67 healthy volunteers at West China Hospital of Sichuan University from January to July 2023. Imaging was performed by using CS-AI 3D PDWI sequence with acceleration factors (AF) of 4, 6, 8, and 10, respectively. According to the AF, all subjects were divided into 4 groups: CS-AI 4, CS-AI 6, CS-AI 8 and CS-AI 10, with CS-AI 4 serving as a reference. Recording the scan time, the signal and noise intensity of the femoral head, muscle, and subcutaneous fat were measured by a senior radiologist and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were then calculated. Additionally, two observers provided ratings for overall image quality and artifacts in the 4 groups, and statistical analysis was performed using the Friedman rank-sum test.Results:The acquisition times for CS-AI 4, CS-AI 6, CS-AI 8, and CS-AI 10 were 5 min 49 s, 3 min 54 s, 2 min 56 s and 2 min 22 s, respectively. Compared to CS-AI 4, the scanning time for CS-AI 6, CS-AI 8, CS-AI 10 were reduced by 32.95%, 50.14%, 59.31%, respectively. The objective evaluation revealed that the SNR and CNR of the femoral head and muscle in groups CS-AI 6, CS-AI 8, and CS-AI 10 were slightly lower than those in group CS-AI 4 ( P<0.05), and the differences were statistically significant. However, no statistically significant differences were found among the 3 groups ( P>0.05). The subjective evaluation indicated that the overall image quality scores of group CS-AI 8 [3 (3,4)] did not significantly differ from those of group CS-AI 4 and CS-AI 6( P>0.05); The mean scores of group CS-AI 4 and CS-AI 6 were 4 (4, 4); Scores of group CS-AI 10 was 3(3, 3), which statistically significant differ from those of the other groups ( P<0.05). The artifacts rating for groups CS-AI 4, CS-AI 6, CS-AI 8 and CS-AI 10 were 4 (4, 4), 4 (4, 4), 3 (3, 4), and 2 (2, 3) respectively. When AF was set to 10, the images exhibited the most severe artifacts ( P<0.05). For other AF values, artifact ratings did not differ significantly ( P>0.05). Conclusion:The CS-AI 3D-PDWI sequence with acceleration factor 8 can acquire high-resolution images of the unilateral hip joint that meet clinical diagnostic requirements while reducing scanning time.