Objective A quality control(QC)system based on the electronic portal imaging device(EPID)system was used to realize the Multi-Leaf Collimator(MLC)position verification and dose verification functions on Primus and VenusX accelerators.Methods The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation.The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation.The dose data obtained by EPID and two-dimensional matrix(MapCheck/PTW)were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis.Results The position error of VenusX MLC was less than 1 mm.The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID.For the dose reconstructed by EPID,the average γ passing rates of Primus were 98.86%and 91.39%under the criteria of 3%/3 mm,10%threshold and 2%/2 mm,10%threshold,respectively.The average γ passing rates of VenusX were 98.49%and 91.11%,respectively.Conclusion The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

A dose reconstruction algorithm for electrionic portal imaging device(EPID)based on calibration and calculation is developed.The raw data of EPID in continuous acquisition mode are corrected for dark field and gain,and the gray level features of bright field are used to determine the field boundary.Subsequently,MU calibration,off-axis calibration and field size calibration are performed on the EPID data,and dose reconstruction is carried out based on the calibrated superimposed flux and the Monte Carlo model of the linac head.Nine cases of IMRT plans are selected for verification and measurement using EPID and MapCheck separately,and the passing rates between the two tools are compared under different gamma criteria(3%/3 mm and 2%/2 mm).For a planned case,the average passing rates of multiple cases verified by MapCheck under the two criteria were 99.02%±1.28%and 90.84%±4.49%,and the average passing rates of the EPID reconstruction models were 98.86%±1.19%and 91.39%±4.80%.Compared with MapCheck,the EPID reconstruction algorithm based on calibration and calculation has no significant difference in the passing rate of IMRT plan verification(P＞0.05),which meets the clinical requirements of dose verification.

In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.

Image-guided radiation therapy (IGRT) is a visual image-guided radiotherapy technique that has many advantages such as increasing the dose of tumor target area and reducing the dose of normal organ exposure. Cone beam CT (CBCT) is one of the most commonly used medical images in IGRT, and the rapid and accurate targeting of CBCT and the segmentation of dangerous organs are of great significance for radiotherapy. The current research method mainly includes partitioning method based on registration and segmentation method based on deep learning. This study reviews the CBCT image segmentation method, existing problems and development directions.

Objective:To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility.Methods:Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT).Results:In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion:The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.

Objective:To study and summarize the effectiveness of the case-based three-dimensional teaching method in clinical probation teaching of otolaryngology for international students.Methods:We divided a total of 190 international medical students in grades 2014 and 2015 from Chongqing Medical University in 2014 and 2015 into control group (92 students) and experimental group (98 students). The control group used the traditional case-based teaching method, while the experimental group used the case-based three-dimensional teaching method. After the semester was over, teaching effectiveness was evaluated through comprehensive examination and a questionnaire survey. SPSS 21.0 was used to perform the t-test and Fisher's exact test. Results:The examination score of the experimental group was significantly higher than that of the control group [(81.68±4.45) vs. (77.80±5.10), P<0.05]. The experimental group had a significantly higher degree of satisfaction than the control group ( P<0.05). Conclusion:The case-based three-dimensional teaching method has advantages in improving the learning interest and academic performance of international students.

Objective:To construct a cycle dual-task network based on cycleGAN to implement 3D CT synthesis from single-view projection for adaptive radiotherapy of thoracic tumor and then evaluate image quality and dose accuracy.Methods:A total of 45 thoracic tumor patients admitted to the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University were collected, and 991 cases were also selected from public dataset as pretrained dataset. Multi-view projections were acquired by ASTRA algorithm. The public dataset was divided into a training set of 800 cases, a validation set of 160 cases and a test set of 31 cases. The dataset obtained from patients in our hospital was divided into a training set of 40 cases and a test set of 5 cases. The network included synthetic CT model and multi-view projection prediction model and achieved the dual-task training. The final test only used the synthetic CT model to acquire the predicted CT images and deliver image quality [mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)] and dose evaluation.Results:Image quality evaluation metrics for synthetic CT showed high image synthesis accuracy with MAE of 0.05±0.01, PSNR of 19.08±1.69, SSIM of 0.75±0.04, respectively. The dose distribution calculated on synthetic CT was also close to the actual dose distribution. The mean 3%/3 mm γ pass rate for synthetic CT was 93.1%.Conclusions:A dual-task cycle network modified on cycleGAN has been implemented to rapidly and accurately predict 3D CT from single-view projection, which can be applied to the workflow of adaptive radiotherapy for thoracic cancer. Both image generation quality and dosimetric evaluation demonstrate that synthetic CT can meet the clinical requirements for radiotherapy.

Objective To investigate the dosimetric effect of truncated regions in computed tomography (CT) images on the targets and organs at risk in volumetric modulated arc therapy (VMAT) for middle thoracic esophageal cancer. Methods CT images of 15 patients with middle thoracic esophageal cancer were selected. Circle masks were used to make the volume of the truncated region account for 10%, 20%, 30%, and 40% of the arm volume, and the corresponding truncated CT images were obtained. The real CT was denoted as CT0. Two radiotherapy plans were made on CT0. One plan was VMAT_1F with full arcs, and the other one was VMAT_3F with arm avoidance. The plans were transplanted to four truncated CT, respectively, and the dosimetric differences between different plans were compared using Wilcoxon signed-rank test. Results Compared with VMAT_1F in CT0, D_mean and V₅ of the lung decreased in VMAT_3F, but D_max of the spinal cord, D_mean of the heart, and V₂₀ of the lung increased. In VMAT_3F, there was no statistically significant difference between the dosimetric parameters in the four truncated CT and those in CT0 (all P > 0.05). In VMAT_1F, except for homogeneity index and D_max of the spinal cord, the dosimetric parameters in four truncated CT were significantly different from those in CT0 (P < 0.05). The dosimetric difference increased with the increase in truncated region-to-volume ratio. Conclusion Complete CT data should be collected in clinical practice, and the radiation field avoiding the truncated regionshould be set if necessary to reduce the influence of the truncated region on dosimetry.

With the improvement of the complexity of medical image synthesis and the demand for the accuracy of clinical radiotherapy, deep learning algorithm plays an increasingly important role in pseudo CT image synthesis and analysis. This paper classifies and analyzes the pseudo CT image synthesis technology based on deep learning method in terms of the types of image modes, and describes the ongoing application in radiotherapy.

Objective:To develop a 3D visualization technology-assisted patient positioning system for radiotherapy and compare it with traditional patient positioning method for breast and pelvic radiotherapy.Methods:A total of 40 patients receiving radiotherapy in Changzhou No.2 People′s Hospital from June 2020 to April 2021 were selected for this study, including 20 patients with breast cancer and 20 patients with pelvic cancer.3D visualization reconstruction was carried out using the CT data of the patients for positioning. Then the 3D visualization models were integrated with the real treatment environment and were then shifted to the isocentral positions of accelerators through interactive operations. Based on this, the patients were actually positioned. Every week, all of the patients were firstly treated with traditional positioning, followed by 3D visualization-guided positioning. As a result, 240 times of positioning data of all patients were collected in three weeks. They were compared with the data of cone-beam CT(CBCT)-guided positioning, which served as the gold standard.Results:The absolute positioning errors of 3D visualization-guided positioning along x, y and z axes were (1.92±1.23), (2.04±1.16), and (1.77±1.37)mm, respectively for patients with breast cancer and were (2.07±1.08), (1.33±0.88), and (1.99±1.25)mm, respectively for patients with pelvic cancer. Compared with traditional positioning method , the accuracy of 3D visualization-guided positioning along x、 y, and z axes was increased by 38.83%, 52.40% and 33%, respectively for patients with breast cancer and was improved by 36.84%, 54.04% and 52.58% for patients with pelvic cancer, with all differences being statistically significant along y and z axes ( t=2.956-5.734, P< 0.05). Meanwhile, the error distribution of the two positioning method was statistically significant along in y axis for patients with breast cancer( χ2=7.481, P<0.05) and was statistically significant along each axis for patients with pelvic cancer( χ2=5.900, 6.415, 7.200, P<0.05). Conclusions:The positioning method guided by 3D visualization technology can effectively improve the positioning accuracy of patients with breast cancer and patients with pelvic cancer and is of value in potential clinical application.