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Objective@#To understand the association between waist circumference with psychological sub health among middle school students in Xinjiang, and to provide evidence for the management of waist circumference and effective prevention of mental health problems.@*Methods@#By using the stratified cluster sampling, 6 191 middle school students aged 13-15 in Urumqi, Karamay, Yining, and Kashgar of Xinjiang were surveyed on demographic information, lifestyle, waist circumference, and psychological sub health symptoms. Chi square test, one way ANOVA, and Logistic regression analysis were used to analyze the association between waist circumference and mental sub health.@*Results@#The detection rate of psychological subhealth among secondary school students in Xinjiang was 21.4%. The detection rates of emotional problems, behavioral problems, and social adjustment difficulties were 28.4 %, 28.1%, and 17.7%, respectively. Significant differences in the detection rate of emotional problems, social adjustment difficulties, and psychological sub health scores were observed among students with different waist circumference levels ( F= 4.03 , 10.90 , 5.81, P <0.05). Logistic regression analysis showed that secondary school students with high waist circumference had a higher risk of social adjustment difficulties ( OR=1.45, 95%CI =1.20-1.75) and psychological sub health problems ( OR=1.46, 95%CI =1.22-1.74) compared to those with normal waist circumference ( P <0.01).@*Conclusion@#Psychological sub health problems of middle school students in Xinjiang are prominent, and the waist circumference is positively related to the psychological sub health problems. Schools should pay attention to the mental health education of middle school students, ensure reasonable diet, sleep and living habits, and promote their physical and mental health development.
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OBJECTIVE@#To extend the application of Gibbs artifact reduction method that exploits local subvoxel- shifts (LSS) to zero- padded k-space magnetic resonance imaging (MRI) data.@*METHODS@#We investigated two approaches to extending the application of LSS-based method to under-sampled data. The first approach, namely LSS+ interpolation, utilized the original LSS-based method to minimize the local variation on nonzero-padding reconstructed images, followed by image interpolation to obtain the final images. The second approach, interlaced local variation, used zero-padded Fourier transformation followed by elimination of Gibbs artifacts by minimizing a novel interlaced local variations (iLV) term. We compared the two methods with the original LSS and Hamming window filter algorithms, and verified their feasibility and robustness in phantom and data.@*RESULTS@#The two methods proposed showed better performance than the original LSS and Hamming window filters and effectively eliminated Gibbs artifacts while preserving the image details. Compared to LSS + interpolation method, iLV method better preserved the details of the images.@*CONCLUSIONS@#The iLV and LSS+interpolation methods proposed herein both extend the application of the original LSS method and can eliminate Gibbs artifacts in zero-filled k-space data reconstruction images, and iLV method shows a more prominent advantage in retaining the image details.