Objective To explore the factors influencing the image quality of thyroid CT and the application value of optimization schemes in reducing streak artifacts in thyroid CT examinations.Methods A retrospective review of the data of 141 patients who underwent thyroid enhanced CT examinations was conducted to analyze the factors affecting image quality.After determining the influencing factors,a self-controlled study was conducted on 48 patients who underwent thyroid CT examinations to analyze the feasibility of the optimization scheme.Results The patients'body mass index(BMI),neck circumference,pharynx-jaw angle,and pharynx-jaw angle-to-neck circumference ratio showed statistically significant differences in their impact on image quality(P<0.05).Compared to the conventional scheme,the optimization scheme showed significantly higher subjective and objective image evaluations.The optimization scheme also significantly improved the diagnostic performance of the images,with statistically significant differences(P<0.05).There was no statistically significant difference in radiation dose between the two positions(P>0.05).Conclusion The optimization scheme can effectively reduce streak artifacts in thyroid CT examinations.

Objective To investigate the influence of different contrast agent injection schemes on imaging quality in neck enhanced CT examination.Methods A total of 150 patients who underwent neck enhanced CT examination were selected and randomly divided into three groups:groups A,B,and C,with 50 cases in each group.Group A adopted individualized injection scheme,group B used three-phase dual-flow contrast agent injection scheme,and group C used measured constant-speed injection scheme,respectively.The objective and subjective evaluation methods were used to assess the image quality of cervical vessel and non-vessel structures.Results Among the three groups of contrast agent injection schemes,the CT values of group A and group B were higher than those of group C except for the aortic arch and submandibular gland in objective evaluation,and the contrast-to-noise ratio(CNR)of group A and group B except for the left common carotid artery and right common carotid artery bifurcation was higher than that of group C.In subjective evaluation,there was no statistically significant difference in image quality among the three groups(P＞0.05),and group B effectively reduced the impact of hardening artifacts.Conclusion The post-injection dual-flow contrast agent injection scheme can achieve the same enhancement effect as other injection schemes while ensuring image quality and reducing the amount of contrast agent used in the examination.

This paper explores the application of multimodal affective brain-computer interfaces(aBCI) in the diagnosis based on the objective assessment of depression and treatment of deep brain stimulation for refractory depression. In the objective assessment of depression, the traditional depression scales are transformed into the interactive affective tasks. Based on the multimodal aBCI systems, multimodal physiological data including EEG, eye movement, etc, are collected simultaneously. Through deep learning, such as multimodal fusion and transfer learning, an objective assessment systems that can acurately distinguish depression states is established. In the treatment of refractory depression with deep brain stimulation, based on multimodal aBCI and reinforcement learning algorithms, the autonomously adjustment and personalization of the parameters are possible to improve the treatment outcomes.

This paper explores the application of multimodal affective brain-computer interfaces(aBCI) in the diagnosis based on the objective assessment of depression and treatment of deep brain stimulation for refractory depression. In the objective assessment of depression, the traditional depression scales are transformed into the interactive affective tasks. Based on the multimodal aBCI systems, multimodal physiological data including EEG, eye movement, etc, are collected simultaneously. Through deep learning, such as multimodal fusion and transfer learning, an objective assessment systems that can acurately distinguish depression states is established. In the treatment of refractory depression with deep brain stimulation, based on multimodal aBCI and reinforcement learning algorithms, the autonomously adjustment and personalization of the parameters are possible to improve the treatment outcomes.