Objective:To explore the capability of deep learning image reconstruction (DLIR) compared to adaptive statistical iterative reconstruction (ASiR-V) in improving the image quality and myocardial edge sharpness of dynamic stress myocardial CT perfusion imaging (CTP).Methods:Thirty subjects who underwent dynamic stress myocardial CTP at Guangdong Provincial People′s Hospital from September 2023 to February 2024 were recruited. Image data of all enrolled patients were reconstructed using ASiR-V 50%, ASiR-V 80%, medium-intensity DLIR(DLIR-M), and high-intensity DLIR(DLIR-H), respectively. Regions of interest were selected in the left ventricular cavity, interventricular septum, and left ventricular lateral wall for measurement of CT values and standard deviations (SD), and calculation of signal to noise ratio (SNR) and contrast to noise ratio (CNR). Matlab was utilized to obtain the differences (d) and slopes (s) of CT value changes at four left ventricular myocardial edges for objective edge sharpness evaluation. Two radiologists subjectively scored the images for noise, natural appearance, and edge sharpness. In case of disagreement between the two radiologists, a third senior radiologist′s score was decisive. Left ventricular myocardial blood flow (MBF) of ASiR-V and DLIR images with lower SD, higher SNR and CNR were calculated, respectively. When the normal distribution was satisfied, the independent sample t test was used for comparison between two groups, and the random block design ANOVA was used for comparison between multiple groups. And analysis was conducted using Friedman test for non-normally distributed data, and Bonferroni correction for pairwise comparisons. Results:There were statistically significant differences in SD, SNR, and CNR among the four images in the interventricular septum and left ventricular lateral wall (all P<0.05), with ASiR-V 80% and DLIR-H demonstrating the lowest SD, highest SNR and CNR, and the subjective image noise score. Statistically significant differences were observed in d and s for the four left ventricular myocardial edges (all P<0.05), with DLIR-M and DLIR-H exhibiting the best objective edge sharpness [5 (5, 5)], and ASiR-V 80% the worst [3.5 (3, 4)]. In the subjective scores for natural appearance, DLIR-M and DLIR-H received the highest scores [5 (5, 5)], while ASiR-V 80% received the lowest scores [3 (3, 4)], with statistically significant differences (all P<0.05). There was no statistically significant difference in MBF values calculated from ASiR-V 80% and DLIR-H images (all P>0.05). Conclusions:The SD value, SNR and CNR of dynamic stress myocardial CTP images reconstructed by DLIR-H are equivalent to ASiR-V 80%, and using DLIR-H can improve the edge sharpness of left ventricular myocardium without affecting the calculation of MBF.

Objective:To explore the capability of deep learning image reconstruction (DLIR) compared to adaptive statistical iterative reconstruction (ASiR-V) in improving the image quality and myocardial edge sharpness of dynamic stress myocardial CT perfusion imaging (CTP).Methods:Thirty subjects who underwent dynamic stress myocardial CTP at Guangdong Provincial People′s Hospital from September 2023 to February 2024 were recruited. Image data of all enrolled patients were reconstructed using ASiR-V 50%, ASiR-V 80%, medium-intensity DLIR(DLIR-M), and high-intensity DLIR(DLIR-H), respectively. Regions of interest were selected in the left ventricular cavity, interventricular septum, and left ventricular lateral wall for measurement of CT values and standard deviations (SD), and calculation of signal to noise ratio (SNR) and contrast to noise ratio (CNR). Matlab was utilized to obtain the differences (d) and slopes (s) of CT value changes at four left ventricular myocardial edges for objective edge sharpness evaluation. Two radiologists subjectively scored the images for noise, natural appearance, and edge sharpness. In case of disagreement between the two radiologists, a third senior radiologist′s score was decisive. Left ventricular myocardial blood flow (MBF) of ASiR-V and DLIR images with lower SD, higher SNR and CNR were calculated, respectively. When the normal distribution was satisfied, the independent sample t test was used for comparison between two groups, and the random block design ANOVA was used for comparison between multiple groups. And analysis was conducted using Friedman test for non-normally distributed data, and Bonferroni correction for pairwise comparisons. Results:There were statistically significant differences in SD, SNR, and CNR among the four images in the interventricular septum and left ventricular lateral wall (all P<0.05), with ASiR-V 80% and DLIR-H demonstrating the lowest SD, highest SNR and CNR, and the subjective image noise score. Statistically significant differences were observed in d and s for the four left ventricular myocardial edges (all P<0.05), with DLIR-M and DLIR-H exhibiting the best objective edge sharpness [5 (5, 5)], and ASiR-V 80% the worst [3.5 (3, 4)]. In the subjective scores for natural appearance, DLIR-M and DLIR-H received the highest scores [5 (5, 5)], while ASiR-V 80% received the lowest scores [3 (3, 4)], with statistically significant differences (all P<0.05). There was no statistically significant difference in MBF values calculated from ASiR-V 80% and DLIR-H images (all P>0.05). Conclusions:The SD value, SNR and CNR of dynamic stress myocardial CTP images reconstructed by DLIR-H are equivalent to ASiR-V 80%, and using DLIR-H can improve the edge sharpness of left ventricular myocardium without affecting the calculation of MBF.

Objective To establish a risk prediction model of diabetic retinopathy (DR) for type 2 diabetic patients (T2DM).Methods A total of 315 T2DM patients (600 eyes) were enrolled in the study.There were 132 males (264 eyes) and 183 females (366 eyes).The mean age was (67.28± 12.17) years and the mean diabetes duration was (10.86 ± 7.81) years.The subjects were randomly assigned to model group and check group,each had 252 patients (504 eyes) and 63 patients (126 eyes) respectively.Some basic information including gender,age,education degree and diabetes duration were collected.The probable risk factors of DR including height,weight,blood pressure,fasting glucose,glycosylated hemoglobin (HbAlc),blood urea,serum creatinine,uric acid,triglyceride,total cholesterol,high-density lipoprotein,low density lipoprotein cholesterol and urinary protein.The fundus photograph and the axial length were measured.Multivariate logistic regression was used to analyze the correlative factors of DR and establish the regression equation (risk model).Receiver operating characteristic (ROC) curves were used to determine the cut-off point for the score.The maximum Youden Index was used to determine the threshold of the equation.The check group was used to check the feasibility of the predictive model.Results Among 504 eyes in the model group,170 eyes were DR and 334 eyes were not.Among 126 eyes in the check group,45 eyes were DR and 81 eyes were not.Multivariate logistic regression analysis revealed that axial length [β=-0.196,odds ratio (OR)=0.822,P＜0.001],age (β=-0.079,OR=0.924,P＜0.001),diabetes duration (β=0.048,OR=1.049,P=0.001),HbAlc (β=0.184,OR=1.202,P=0.020),urinary protein (β=1.298,OR=3.661,P＜0.001) were correlated with DR significantly and the simplified calculation of the score of DR were as follows:P=7.018-0.196X 1-0.079X2+0.048X3+0.148X4+ 1.298X5 (X1=axial length,X2=age,X3=diabetes duration,X4=glycosylated hemoglobin,X5=urinary protein).The area under the ROC curve for the score DR was 0.800 and the cut-offpoint of the score was-1.485.The elements of the check group were substituted into the equation to calculate the scores and the scores were compared with the diagnostic threshold to ensure the patients in high-risk of DR.The result of the score showed 84％ sensitivity and 59％ specificity.ROC curve for the score to predict DR was 0.756.Conclusion Axial length,age,diabetes duration,HbA1c and urinary protein have significant correlation with DR.The sensitivity and specificity of the risk model to predict DR are 84.0％ and 59.0％ respectively.The area under the ROC curve was 0.756.

Objective To investigate the influence of humanitarian care on the depression and the anxiety of patients before they receive their abdominal surgery. Methods 98 patients were randomly derided into the experi-ment group and the control group and each group consisted of 49 patients. Humanitarian care was conducted in the ex-periment group while the control group only implemented the traditional functional nursing. Self-rating Depression Scale and Self-rating Anxiety Scale were used as the evaluation tools. Results The depression and the anxiety values of patients in the experiment group were obviously lower than those in the control group. Comparing both groups (P <0.05), such difference was of statistical significance. Conclusion The humanitarian care can reduce the feeling of depression and anxiety of patients, which is good for the recovery after surgery.

Objective To assess the effect of relaxation training combined with listening to music on postoperative rehabilitation of patients with liver cancer. Methods Patients who met the inclusion and exclusion criteria (52 cases) were divided into the experimental group and the control group with 26 cases in each group.The control group received routine postoperative nursing.The experimental group was given relaxation training combined with listening to music based upon routine nursing.The pain degree,time of ambulation,recovery of gastrointestinal function and bladder function and related postoperative complica-tions were compared between the two groups. Results The experimental group had less pain,earlier time of ambulation,shorter time for recovery of gastrointestinal function, indwelling gastric tube and urinary catheter(P < 0.01 ).Incidence rate of incision,bile leakage and intra-abdominal hemorrhage were not statis-tically different between the two groups (P > 0.05). Conclusion Relaxation training combined with lis-tening to music can facilitate the postoperative rehabilitation process of patients with liver cancer.