Objective To construct a global model of cardiac surface motion based on average intensity projection(AIP)image.Methods The cine magnetic resonance imaging data were divided into training set and test set for model construction and validation.The datum points were obtained on the AIP surface point cloud,and the corresponding points of each cardiac phase surface point cloud were found based on surface features.Principal component analysis was used to extract feature information,establish the mapping relationship between the datum points and the corresponding points,and construct a model for predicting each temporal phase surface point cloud from the AIP surface point cloud.Results The RMSE of the model on test set for corresponding point prediction ranged from(0.209±0.020)mm to(0.841±0.074)mm,while the Euclidean distance for each time phase surface point cloud prediction ranged from(1.399±0.029)mm to(1.658±0.100)mm.Conclusion The proposed global model exhibit high accuracy and can provide a reference for image segmentation and clinical treatments.

Objective To construct a global model of cardiac surface motion based on average intensity projection(AIP)image.Methods The cine magnetic resonance imaging data were divided into training set and test set for model construction and validation.The datum points were obtained on the AIP surface point cloud,and the corresponding points of each cardiac phase surface point cloud were found based on surface features.Principal component analysis was used to extract feature information,establish the mapping relationship between the datum points and the corresponding points,and construct a model for predicting each temporal phase surface point cloud from the AIP surface point cloud.Results The RMSE of the model on test set for corresponding point prediction ranged from(0.209±0.020)mm to(0.841±0.074)mm,while the Euclidean distance for each time phase surface point cloud prediction ranged from(1.399±0.029)mm to(1.658±0.100)mm.Conclusion The proposed global model exhibit high accuracy and can provide a reference for image segmentation and clinical treatments.

Objective:To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring.Methods:CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed.Results:The model had a high prediction success rate for the target position. The variance explainded and root mean squared error ( RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion:The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.

Objective:To propose a markerless beam's eye view (BEV) tumor tracking algorithm, which can be applied to megavolt (MV) images with poor image quality, multi-leaf collimator (MLC) occlusion and non-rigid deformation.Methods:Window template matching, image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images. The quality assurance (QA) plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator, and 682 electronic portal imaging device (EPID) images in the treatment process were collected as fixed images. Meanwhile, the digitally reconstructured radiograph (DRR) images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm. In addition, a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study, providing quantitative results of tumor location changes during treatment. Image similarity was used for third-party verification of tracking results.Results:The algorithm could cope with different degrees (10%-80%) of image missing. In the phantom verification, 86.8% of the tracking errors were less than 3 mm, and 80% were less than 2 mm. Normalized mutual information (NMI) varied from 1.182±0.026 to 1.202±0.027 ( P<0.005) before and after registration and the change of Hausdorff distance (HD) was from 57.767±6.474 to 56.664±6.733 ( P<0.005). The case results were predominantly translational (-6.0 mm to 6.2 mm), but non-rigid deformation still existed. NMI varied from 1.216±0.031 to 1.225±0.031 ( P<0.005) before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089 ( P<0.005). Conclusions:The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies. It provides a reference idea for processing MV images with multi-modality, partial data and poor image quality.

Objective:To propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.Methods:This study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.Results:The algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration ( t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration ( t = -2.91, P = 0.001). Conclusions:The tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.

Objective:To propose a method of image similarity measurement based on structure information and intuitionistic fuzzy set and measure the similarity between CT image and CBCT image of radiotherapy plan positioning, aiming to objectively measure the setup errors.Methods:A total of four pre-registration images of a nasopharyngeal carcinoma patient on the cross-sectional and sagittal planes and a pelvic tumor patient on the cross-sectional and coronal planes were randomly selected. Five methods were used to quantify the setup errors, including correlation coefficient, mean square error, image joint entropy, mutual information and similarity measure method.Results:All five methods could describe the deviation to a certain extent. Compared with other methods, the similarity measure method showed a stronger upward trend with the increase of errors. After normalization, the results of five types of error increase on the cross-sectional plane of the nasopharyngeal carcinoma patient were 0.553, 0.683, 1.055, 1.995, 5.151, and 1.171, 1.618, 1.962, 1.790, 3.572 on the sagittal plane, respectively. The results of other methods were between 0 and 2 after normalization, and the results of different errors of the same method slightly changed. In addition, the method was more sensitive to the soft tissue errors.Conclusions:The image similarity measurement method based on structure information and intuitionistic fuzzy set is more consistent with human eye perception than the existing evaluation methods. The errors between bone markers and soft tissues can be objectively quantified to certain extent. The soft tissue deviation reflected by the setup errors is of significance for individualized precision radiotherapy.

Objective:To investigate the clinicopathological characteristics of gastric adenocarcinoma of the fundic gland(GA-FG).Methods:A total of 6 patients with GA-FG admitted from January 2014 to May 2019 were identified at Shanghai Songjiang District Central Hospital.Analyzed endoscopic findings, pathological characteristics and immunohistochemical staining of the lesions and reviewed relevant literatures.Results:Among the 6 patients, 3 were male, 3 were female, and the mean age was 66 years. The most common clinical presentation was epigastric discomfort. Gastroscopic examination revealed an elevated or flat lesion in the body of the stomach, with a diameter of 0.4 to 1.0 cm. Histopathologically, they mainly composed of main cells, but there were also scattered parietal cells, arranged in an irregular tubular or cord-like structure, and formed anastomosis branches. The tumor cells were immunohistochemically positive for MUC6 and pepsinogen 1.Conclusions:GA-FG is a rare variant of well-differentiated adenocarcinoma. It is important to understand its clinical features and histological morphology for more accurate diagnosis.

Objective: To quantitatively evaluate dose accuracy of radiotherapy for cervical cancer. Methods: A CT image correction algorithm based on image transformation was proposed. Referring to CBCT images, CT images of radiotherapy plan for cervical cancer were corrected to obtain the corrected images which could reflect the actual body position of treatment. The clinical plan was transplanted to the corrected images for dose recalculation as a test plan, and the dosimetry parameters were statistically compared to evaluate the dose accuracy. Results: Both of the target coverage of contrast plans could meet the clinical requirements (>98%), and there was no significant difference in the homogeneity index (P=0.150). The conformability of the test plan was significantly worse than that of the clinical plan (P<0.05). The maximum dose of each organ at risk in the test plan was approximately 30 cGy higher than that of the clinical plan (P<0.05), V₅₀ was slightly higher than that of the clinical plan, whereas the average dose (D_mean) did not significantly differ. Conclusion The CT image correction algorithm based on image transformation can quantitatively evaluate the dose accuracy of radiotherapy for cervical cancer, which provides reference for resolving similar problems in clinical practice.

Objective Newborn bilirubin encephalopathy seriously threatens the life and health of newborns, Both the mortality and morbidity are high.Cerebrospinal fluid bilirubin concentration can better reflect the blood-brain barrier function and brain bilirubin levels, which can help the diagnosis of bilirubin encephalopathy.Methods In order to meet the clinical needs, we confirmed the detection performance of the existing serum bilirubin detection system for cerebrospinal fluid bilirubin, including precision, accuracy and analysis of measurement range.Results The results showed that the detection system was linear at 1.0-25.1μmol/L, and the daytime CVat normal and pathological values was less than the precision requirement (6％) .The bias of each sample and the average bias are less than the allowable bias (5％) , the total error is less than the total allowable error (15％) .Conclusion Therefore, the performance of the detection system meet the industry standards, can be used for neonatal cerebrospinal fluid bilirubin detection.

Objective To construct a knowledge system of cultural competence education among undergraduate nursing students, so as to enhance the cultural capacity of undergraduate nursing students. Methods From September 2015 to January 2016, a two-round expert consultation was conducted among 11 nursing experts who engaged in multicultural related work in Shanghai, Hangzhou and other places by Delphi method. Results The active coefficient of the two rounds of the expert consultation were both 100%. The expert authority (Cr) was 0.89, and the coordination coefficients of the first and second round of consultation were 0.227 and 0.236 respectively (P<0.01). Finally, the knowledge system of cultural competence education in undergraduate nursing students was constructed, including 5 first-level indicators, 10 second-level indicators and 39 third-level indicators. Conclusions The primarily constructed knowledge system of cultural competence education among undergraduate nursing students is a scientific and reasonable frame work system, which provides content references and evaluation basis for the cultural competence education among undergraduate nursing students in our country.