Medical imaging technology plays a crucial role in clinical diagnosis and treatment. Image compression technology provides robust technical support for the storage and transmission of massive medical imaging data, serving as an effective safeguard for hospital data backup and telemedicine. The technology holds broad application prospects in the medical field, enabling the processing of various imaging modalities, multidimensional imaging, and medical video imaging. This study elaborates on general image and video compression algorithms, the application of compression algorithms in the medical field, and the performance metrics of medical image compression, thereby providing critical technical support for enhancing clinical diagnostic efficiency and data management security.

Objective:To establish a radiotherapy treatment planning process of high ventilation functional lung avoided (HVFLA) for thoracic tumors based on 4D-CT lung ventilation functional images and determine the treatment planning strategy of HVFLA radiotherapy, and so as to provide support for the clinical trials of HVFLA radiotherapy in thoracic cancer patients.Methods:A deep learning-based 4D-CT lung ventilation functional imaging model was established and integrated into the radiotherapy treatment planning process. Furthermore, ten thoracic cancer patients with 4D-CT simulation positioning were retrospectively enrolled in this study. The established model was used to obtain the 4D-CT lung ventilation functional imaging for each patient. According to the relative value of lung ventilation, the lung ventilation areas are equally segmented into high, medium and low lung ventilation and then imported them into Pinnacle 3 treatment planning system. According to the prescription dose of target and dose constraints of organ at risks (OARs), the clinical and HVFLA treatment plans were designed for each patient using volumetric modulated radiotherapy technique, and each plan should meet the clinical requirements and adding dose constraints of high ventilation functional lung for HVFLA plan. The dosimetric indexes of the target, OARs (lungs, heart and cord) and high functional lung (HFL) were used to evaluated the plan quality. The dosimetric indexes included D2, D98 and mean dose of target, V5, V10, V20, V30 and mean dose of lungs and HFL, V30, V40 and mean dose of heart, and D1 cm 3 of cord. Paired samples t-test was used for statistical analysis of the two groups of plans. Results:The target and OARs of the clinical plan and HVFLA plan meet the clinical requirements. The HVFLA plan resulted in a statistically significant reduction in the mean dose, V5, V10, V20, and V30 of the high functional lung by 1.2 Gy, 5.9%, 4.2%, 2.6%, and 2.3%, respectively ( t=-8.07, 4.02, -6.02, -7.06, -6.77, P<0.05). There was no statistical difference in the dosimetric indexes of lungs, heart and cord. Conclusions:We established the treatment planning process of HVFLA radiotherapy based on 4D-CT lung ventilation functional images. The HVFLA plan can effectively reduce the dose of HFL, while the doses of lungs, heart and cord had no significant difference compared with the clinical plan. The strategy of HVFLA radiotherapy planning is feasible to provide support for the implementation of HVFLA radiotherapy in thoracic cancer patients.

Objective:To verify the feasibility of using Elekta accelerated go live (AGL) standard process for the acceptance of multiple accelerators.Methods:The beams of three accelerators were adjusted by PTW Beamscan three-dimensional water tank to reach the AGL standard. Dose verification was performed for three accelerators that met AGL standards. A simple field test example from Cancer Hospital Chinese Academy of Medical Sciences was used to compare the MapCheck 3 surface dose measurement results with the surface dose calculated by the same accelerator model. Images of 10 patients including head and neck, esophagus, breast, lung and rectum were randomly selected. volumetric-modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) treatment techniques were used for planning design, and the measured dose of ArcCheck was compared with the planned dose calculated by the same accelerator model. One-way ANOVA was used to statistically analyze the passing rates of two-dimensional and three-dimensional dose verification.Results:The 6 MV X-ray percentage depth dose at 10 cm underwater (PDD 10) of three accelerators was 67.45%, 67.36%, 67.47%, and the maximum deviation between the three accelerators was 0.11%. The 6 MV flattenting filter free (FFF) mode X-ray PDD 10 was 67.33%, 67.20%, 67.20%, and the maximum deviation between the three accelerators was 0.13%. All required discrete point doses on each energy 30 cm×30 cm Profile spindle of the three accelerator X-rays deviated less than ±1% from the standard data. Absolute γ analysis was performed on the results of MapCheck 3 two-dimensional dose matrix validation. Under the 10% threshold of 2 mm/3% standard, the average passing rate of the test cases in Cancer Hospital Chinese Academy of Medical Sciences was above 99%, and the difference was not statistically significant ( P>0.05). Absolute γ analysis was performed on the ArcCheck verification results. Under the 10% threshold, the pass rate of 2 mm/3% was all above 95%, the maximum average passing rate of the three accelerators with different energy and different treatment techniques was 0.28% (6 MV, VMAT), 0.19%(6 MV FFF, VMAT), 0.56% (6 MV, IMRT) and 0.05% (6 MV FFF, IMRT), and the difference was not statistically significant ( P>0.05). Conclusion:Compared with traditional accelerator acceptance process, the acceptance time of each accelerator is shortened by 4-6 weeks by using the AGL standard process, and the radiotherapy plan of patients can be interchangeably executed among different accelerators.

Objective:To develop a deep learning method to predict the anatomical images of nasopharyngeal carcinoma patients during the treatment course, which could detect the anatomical variation for specific patients in advance.Methods:Imaging data including planning CT (pCT) and cone-beam CT (CBCT) for each fraction of 230 patients with T 3-T 4 staging nasopharyngeal carcinoma who treated in Cancer Hospital Chinese Academy of Medical Sciences from January 1, 2020 to December 31, 2022 were collected. The anatomical images of week k+1 were predicted using a 3D Unet model with inputs of pCT, CBCT on days 1-3, and CBCT of weeks 2- k. In this experiment, we trained four models to predict anatomical images of weeks 3-6, respectively. The nasopharynx gross tumor volume (GTV nx) and bilateral parotid glands were delineated on the predicted and real images (ground truth). The performance of models was evaluated by the consistence of the delineation between the predicted and ground truth images. Results:The proposed method could predict the anatomical images over the radiotherapy course. The contours of interest in the predicted image were consistent with those in the real image, with Dice similarity coefficient of 0.96, 0.90, 0.92, mean Hausdorff distance of 3.28, 4.18 and 3.86 mm, and mean distance to agreement of 0.37, 0.70, and 0.60 mm, for GTV nx, left parotid, and right parotid, respectively. Conclusion:This deep learning method is an accurate and feasible tool for predicting the patient's anatomical images, which contributes to predicting and preparing treatment strategy in advance and achieving individualized treatment.

Objective　To timely understand the current status and distribution of nail snails (Oncomelania hupensis) in Tongjiang River channels in Zhenjiang City, providing a scientific basis for achieving the goal of eliminating schistosomiasis. Methods　From 2019 to 2023, nine Tongjiang River channels on the south bank of the Yangtze River under the jurisdiction of Zhenjiang City were selected as the monitoring area. Snail monitoring was carried out onshore and beach nail snails floats in the Tongjiang River channels, nail snails on attachments in hardened areas, snails induced by straw curtains, and snails carried by boats and domestic animals. Results　The monitoring results of shoreline snail from 2019 to 2023 showed that the snail situation in the Tongjiang River channel and its outer river bank remained relatively stable from 2019 to 2020; however, in 2021, under the influence of the Yangtze River flooding disaster in 2020, the area of snails increased significantly. In 2021, the area with snails in the Tongjiang River channel and the outer river bank increased by 45.70% (11.95/26.15) and 100.00% (20.00/20.00) compared to 2020; the average density of nails snails in the Tongjiang River channel and the outer river bank and the emergence rate of snail frames both showed a significant increase, rising by 94.73% (0.18/0.19) and 68.08% (8.68/12.75) compared to 2020, and by 122.73% (0.81/0.66) and 102.78% (43.26/42.09), respectively. The differences in the increase in the occurrence rate of spiked frames in the Tongjiang River channel Chili River and Renmin River were not statistically significant (χ2=0.329, P>0.05; χ2=0.646, P>0.05). From 2022 to 2023, the density of nail snails and the occurrence rate of framed snails in the Tongjiang River channel showed a decreasing trend (F=4.72, P=0.04 and χ2=372.58, P<0.01). The area of nail snails, density of live snails, and occurrence rate of framed snails in the outer river bank showed a decreasing trend (F=13.96, P=0.02; F=23.43, P<0.01; χ2=1 029.69, P<0.01). During the five years, no nail snails were detected in the ancient canal and 11 tributaries. From 2019 to 2023, 180 times of 3 003 kg of floating objects were salvaged, with a total of 148 live snails detected. A total of 17 live snails were captured on attachments in the hardened berm area; a total of 11 live snails were captured by straw curtain snail baiting;112 boats were inspected, and no snails were found; there were 112 boats surveyed, and no snails were found; 97 cattle were observed, and 2 cattle were found to carry 1 live snail on their hooves; 321 sheep were observed, and no snails were found on their hooves; and no infectious snails were found in the monitoring area in 5 years. Conclusions　Nail snails continue to exist in the Tongjiang River channel, and the risk factors for schistosomiasis transmission have not been completely eliminated. It is still necessary to carry out accurate monitoring of the snail situation in the Tongjiang River and the river bank, so as to grasp the risk of transmission in time and take emergency measures.

Objective:To evaluate the feasibility of using MRI-only simulation images for dose calculation of both photon and proton radiotherapy for nasopharyngeal carcinoma cases.Methods:T 1-weighted MRI images and CT images of 100 patients with nasopharyngeal carcinoma treated with radiotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from January 2020 to December 2021 were retrospectively analyzed. MRI images were converted to generate pseudo-CT images by using deep learning network models. The training set, validation set and test set included 70 cases, 10 cases and 20 cases, respectively. Convolutional neural network (CNN) and cycle-consistent generative adversarial neural network (CycleGAN) were exploited. Quantitative assessment of image quality was conducted by using mean absolute error (MAE) and structural similarity (SSIM), etc. Dose assessment was performed by using 3D-gamma pass rate and dose-volume histogram (DVH). The quality of pseudo-CT images generated was statistically analyzed by Wilcoxon signed-rank test. Results:The MAE of the CNN and CycleGAN was (91.99±19.98) HU and (108.30±20.54) HU, and the SSIM was 0.97±0.01 and 0.96±0.01, respectively. In terms of dosimetry, the accuracy of pseudo-CT for photon dose calculation was higher than that of the proton plan. For CNN, the gamma pass rate (3 mm/3%) of the photon radiotherapy plan was 99.90%±0.13%. For CycleGAN, the value was 99.87%±0.34%. The gamma pass rates of proton radiotherapy plans were 98.65%±0.64% (CNN, 3 mm/3%) and 97.69%±0.86% (CycleGAN, 3 mm/3%). For DVH, the dose calculation accuracy in the photon plan of pseudo-CT was better than that of the proton plan.Conclusions:The deep learning-based model generated accurate pseudo-CT images from MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for radiotherapy of nasopharyngeal cancer. However, compared with the raw CT images, the error of the CT value in the nasal cavity of the pseudo-CT images was relatively large and special attention should be paid during clinical application.

Objective:To analyze the relationship between volumetric-modulated arc therapy (VMAT) plan delivery time and machine parameters, aiming to exploring the methods of shortening plan delivery time by improving machine performance.Methods:A total of 25 treatment plans of patients who completed VMAT in Cancer Hospital of Chinese Academy of Medical Sciences from March 2017 to July 2017 were selected by simple random sampling. The machine parameters were subject to modeling by using software. For conventional fraction and hyperfraction plans, the parameters that have the strongest limitations on delivery time were simulated by observing the relative change of delivery time in increasing the gantry speed, dose rate, multi-leaf collimator (MLC) leaf speed independently. The speed of the parameter which has the strongest limitations was simulated to obtain the dynamic changes of the limitation strength of these three parameters on the treatment time. The measured data were subject to statistical description.Results:For conventional segmentation plans with 2 Gy fractional dose, the gantry speed had the strongest limitations on delivery time and the MLC leaf speed had the strongest limitations on delivery time instead when the gantry speed was increased by 15%. For hyperfraction plans with 5 Gy fractional dose, dose rate had the strongest limitations on delivery time and the gantry speed had the strongest limitations when the gantry speed was increased by 12%.Conclusions:The relationship between VMAT plan delivery time and machine parameters depends on the characteristics of each plan. The relationship between different machine parameters and plan characteristics should be considered when shortening plan delivery time by improving the machine parameters.

Objective On the premise of meeting the image quality requirements of simulated location for pediatric radiotherapy,the simulated localizable CT parameters are optimized through phantom scanning to reduce the radiation dose.Methods CatPhan700 phantom was used to simulate the child's body,Philip 24-row large-aperture spiral simulated localizable CT was performed,and the CT images were obtained by scanning the phantom at different mAs and tube voltages.The mAs range was set at 60-400 mAs,the scanning was performed every 20 mAs interval,and the kV was set at 80,100,and 120 kV.Image evaluation was carried out using parameters such as image noise(N10 and mean SD),uniformity,low contrast resolution,high contrast resolution,and the stabilities of HU values of Air,Acrylic,50%bone,LDPE,20%bone,Teflon,Polystyrene,DelrinTM,Lung,PMP and Water.The CTDIVol and DLP automatically calculated by the simulated localizable CT system were read to evaluate the radiation dose.Results At 100 kV,as mAs increased,both CTDI and DLP showed upward trends,and the fitting results were linear correlated,with slopes of 0.034 5 and 0.932 4.Image noise was decreased nonlinearly with the increasing mAs.When mAs increased from 60 to 140 mAs,N10 decreased from 0.25%to 0.14%,and SD reduced from 3.74 HU to 2.54 HU.When mAs reached 180 mAs or higher,N10 fluctuated between 0.1%and 0.12%,the mean SD fluctuated between 2.0 and 2.5 HU,and the downward trends obviously slowed down.When mAs increased from 60 to 200 mAs,the low contrast resolution of the image dropped from 0.53 to 0.29.The image uniformity,high contrast resolution and HU values of different substances were less affected by mAs.The image quality of 100 kV and 200 mAs scanning was close to that of 120 kV scanning,but the image quality of 80 kV scanning failed to meet the clinical requirements.Conclusion In order to reduce the radiation dose as much as possible,the mAs should be set at 200 mAs when the tube voltage is set at 100 kV for a simulated cylinder with a diameter of 20 cm.In the actual simulation scanning for pediatric radiotherapy,the scanning parameters should be fine-tuned according to the phantom results and the actual physical characteristics of children to satisfy the optimization principle for radiation protection.

Objective To improve the performance of auto-segmentation of prostate target area and organs-at-risk in online magnetic resonance image and enhance the efficiency of magnetic resonance imaging-guided adaptive radiotherapy(MRIgART)for prostate cancer.Methods A retrospective study was conducted on 40 patients who underwent MRIgART for prostate cancer,including 25 in the training set,5 in the validation set,and 10 in the test set.The planning CT images and corresponding contours,along with online MR images,were registered and input into a deep learning network for online MR image auto-segmentation.The proposed method was compared with deformable image registration(DIR)method and single-MR-input deep learning(SIDL)method.Results The overall accuracy of the proposed method for auto-segmentation was superior to those of DIR and SIDL methods,with average Dice similarity coefficients of 0.896 for clinical target volume,0.941 for bladder,0.840 for rectum,0.943 for left femoral head and 0.940 for right femoral head,respectively.Conclusion The proposed method can effectively improve the accuracy and efficiency of auto-segmentation in MRIgART for prostate cancer.

Objective To evaluate the effects of the multiple factors especially image geometric accuracy of the imaging system on the segmentations of target areas and organs-at-risk.Methods The study used phantoms to test the imaging performance of the 1.5T magnetic resonance(MR)linear accelerator system,including the assessments of MR image geometric distortion and the segmentation errors caused by factors such as image geometric distortion.Model 604-GS large field MR image distortion phantom was used to explore the geometric distortion of the MR images for MR-guided radiotherapy;and CIRS Model 008z upper abdominal phantom was used to analyze the segmentation errors of target areas and organs-at-risk.Results The average geometric distortion and maximum distortion of 3D T1WI-FFE images vs 3D T2WI-TSE images were 0.54 mm vs 0.53 mm and 1.96 mm vs 1.68 mm,respectively;and the control points of the large distortions were distributed at the edges of the phantom,which was consistent with the MR imaging characteristics previously reported.Compared with CT-based segmentation contour,the MDA was 1.17 mm and DSC was 0.91 for 3D T1WI-FFE,while MDA was 0.86 mm and DSC was 0.94 for 3D T2WI-TSE.Conclusion The study quantitatively assesses the geometric accuracy of the imaging system for MR-guided radiotherapy.The phantom-based contour analysis reveals that with CT image as gold standard,the segmentation error in MRI images meets the clinical requirements,and that 3D T2WI-TSE image is advantageous over 3D T1WI-FFE image in segmentation accuracy.