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 propose a novel radiotherapy marking method-the"#"-character method,which aimed at improving the accuracy and repeatability of positioning during radiotherapy.Methods:A specially"cross-shaped"stamp was designed by this study,which consisted of a handheld square base with a"cross-shaped"protrusion.Using this stamp,the extreme positions of end-expiration and end-inspiration were marked respectively at the laser-guided regions on the directly above and bilateral sides of the patient's body,and each position was printed a"+"character.Finally,a"#-shaped"signal was formed,which represented the full range of respiratory motion of patients.The study included two parts:surface displacement caused by respiration was simulated through a three-dimensional(3D)motion platform,which was used to conduct a phantom experiment for anthropomorphic dummy,A randomized controlled study involving 40 patients,who were treated between January and June 2024 at the Department of Radiotherapy,Cancer Hospital,Chinese Academy of Medical Sciences,were conducted.The cohort included 20 patients with breast tumor(Positioning the outer contour by exposing the chest)and 20 patients with thoracic tumor(fixed position of using thermoplastic film).These patients were divided into two groups for comparison,which received respectively the"#-shaped"method and the conventional"+-shaped"method.The cone-beam computed tomography(CBCT)images before treatment were used to compare the influences of the two kinds of marking methods on the positioning errors of patients with breast tumor and patients with thoracic tumor.Then,the statistical analysis was used to assess precision and accuracy of positioning.Results:The result of phantom experiment indicated that the positioning error of the"#-shaped"method was significantly better than that of the"+-shaped"method under various parameters of respiratory movement.Under three kinds of different respiratory cycles(3,4,and 5 seconds)and amplitudes(8,12,and 15 mm),the positioning errors of the"#-shaped"method were respectively(0.15±0.04)cm,(0.19±0.05)cm and(0.35±0.14)cm,while the"+-shaped"method were respectively(0.42±0.16)cm,(0.64±0.28)cm and(0.88±0.37)cm,and the differences were statistically significant(t=8.347,3.416,2.901,P<0.05).The results of actual patients indicated the positioning error[(0.97±0.32)cm]of the"#-shaped"method was significantly lower than[(1.62±0.47)cm]of the"+-shaped"method for patients with breast tumor(Positioning the outer contour by exposing the chest),and the difference was significant(t=3.615,P<0.05).On the other hand,the positioning error[(0.69±0.24)cm]of the"#-shaped"method was significantly lower than[(0.97±0.39)cm]of the"+-shaped"method for patients with thoracic tumor(fixed position of using thermoplastic film),and the difference also was significant(t=1.934,P<0.05).Conclusion:Compared to the conventional"+-shaped"method,the"#-shaped"method appears higher accuracy and repeatability during the positioning process of radiotherapy,which especially is suitable to the treatment for breast tumor and thoracic tumor that need accurately control the influences of respiratory motion.

This study investigated international standard systems for radiotherapy, focusing on the comparison of the radiation protection standards for medical linear accelerators adopted in China, the UK, and the USA. Despite some specific differences, the standards for radiotherapy rooms in the three countries generally adhere to the basic principles set by the International Commission on Radiological Protection (ICRP) and the International Atomic Energy Agency (IAEA). Regarding the zoning principle of radiotherapy rooms, the definitions of the controlled areas are similar in China, the UK, and the USA, while the classification of areas beyond the controlled areas differs across the three countries. In terms of measurement conditions, all the three countries require measurements under the maximum output dose of the radiotherapy equipment, with only minor differences in details. For dose limits and compliance criteria for radiation shielding of radiotherapy rooms, China adopts the highest instantaneous dose rate as the control threshold. In contrast, the UK and the USA base their standards on dose limits evaluated over certain time intervals (annual, weekly, and daily), assessing compliance through workload calculation. In terms of method for deducing and calculating effective dose limits, UK standards stipulate that annual personnel exposure should be calculated using instantaneous dose rates. In comparison, the USA provides specific method for calculating dose rates per week and any one hour from instantaneous dose rates. The comparative analysis indicates that China′s method, which is based on the maximum instantaneous dose rates, may lead to increased construction costs of radiotherapy rooms under the same conditions, hindering the application and development of novel radiotherapy technologies. To address these concerns while maintaining radiation safety, it is recommended that China consider introducing method based on average instantaneous dose rates or calculating the annual personnel exposure doses. This will help optimize protection standards and advance radiotherapy technology.

Objective:To investigate the effects of psychological state on the setup errors of radiotherapy for breast cancer patients.Methods:A prospective cohort study was conducted. A total of 193 breast cancer patients in Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College from October 2022 to May 2023 were selected. Radiotherapy was performed after fixation with an integrated multi-functional device for the head, chest and abdomen. Psychological status of patients was assessed by using 9-item health questionnaire (PHQ-9) and generalized anxiety disorder 7 self-rating scale (GAD-7) before first radiotherapy, the 10th radiotherapy and the last radiotherapy. Based on the results of the questionnaires, patients were divided into psychological problem (anxiety or depression) group and non-psychological problem group. The general data and setup errors of radiotherapy in both groups were compared.Results:All the 193 patients were female, with a median age of 47 years. There were 53 patients in psychological problem group and they underwent a total of 507 image-guided procedures, with setup errors [ M ( Q1, Q3)] of 0.18 (0.07, 0.33), 0.20 (0.10, 0.33) and 0.19 (0.09, 0.30) in the left-right (X), superior-inferior (Y), and anterior-posterior (Z) directions, respectively; the remaining 140 patients in non-psychological problem group underwent 1 240 image-guided procedures, with setup errors [ M ( Q1, Q3)]of 0.17 (0.08, 0.30), 0.20 (0.10, 0.30) and 0.18 (0.09, 0.28) in the X, Y, and Z directions, respectively, and the differences were statistically significant ( Z values were -3.78, -2.00; P < 0.001, P = 0.046). Conclusions:Anxiety and depression have an influence on the setup errors of radiotherapy in patients with breast cancer. In the processs of radiotherapy for breast cancer, it is important to pay attention to the psychological status of patients.

Adaptive radiotherapy (ART) can adjust the treatment plan at one or certain time points during the process, thereby responding to inter-fractional and/or intra-fractional changes in the patient's organs. The ART strategy refers to the specific methods used to adjust the treatment plan in the ART process, proper selection of an ART strategy is crucial to balancing treatment time and dose benefits. Currently, the strategy selection relies mainly on the subjective judgment of medical professionals. In recent years, both domestic and international medical institutions have employed artificial intelligence methods to build prediction models for automatic, fast and accurate selection of ART strategies. This article reviews the strategy selection of online ART, and sequentially introduces the types of strategies currently provided by the ART commercial systems, the current status and existing problems of ART strategy selection by medical institutions, and the present research status and application prospects of automatic ART strategy selection.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

Objective:To evaluate the feasibility of using the domestic ArcherQA system for fast and simplified independent verification of CyberKnife (CK) stereotactic radiotherapy (SRT) plans.Methods:SRT plans of 57 patients treated with CK at Tianjin Medical University Cancer Institute and Hospital from August 2021 to August 2022 were retrospectively analyzed, including 15 intracranial, 30 pulmonary, and 12 abdominal tumors cases. Point-dose and planar-dose verifications were performed using an ionization chamber and radiochromic films embedded in a homogeneous phantom, and the results were compared with those calculated by the treatment planning system (TPS). The localization CT images and corresponding SRT plans were imported into the ArcherQA system for independent dose verification and analysis. The correlation between ArcherQA results and phantom measurements was analyzed, with comparisons of target mean dose differences and γ pass rates.Results:Phantom measurement results showed, the measured point-dose differences for intracranial, lung, and abdominal plans were -0.94% ± 3.22%, 1.92% ± 2.05%, and 2.12% ± 0.77%, respectively. The mean dose differences in target dose calculation between ArcherQA and TPS: intracranial in the gross tumor volume (GTV) regions were 0.34% ± 2.21%, lung tumor GTV were -2.47% ± 2.46%, and abdominal tumor GTV were 0.80% ± 2.61%, respectively. Among them, the abdominal GTV region showed the highest correlation between ArcherQA and measured results ( r=0.78). The average two-dimensional γ pass rates (2 mm/2%, threshold=10%) measured using phantom films were 95.92% ± 2.35% for intracranial, 95.70% ± 2.74% for lung, and 96.74% ± 3.41% for abdominal tumors plans, respectively. The three-dimensional ArcherQA results showed comparable γ pass rates (1 mm/2%, threshold=10%) for lung and abdominal GTV and PTV regions, with similar medians and data dispersion to film measurements. Conclusions:The ArcherQA system enables rapid and efficient independent dose verification of CK SRT plans without the need for additional hardware. The verification results show good correlation with phantom measurements, supporting its potential as an auxiliary quality assurance tool in clinical CK SRT implementation.

Objective To achieve efficient compression of on-board radiotherapy projection images using dynamic video encoding algorithms.Methods The on-board radiotherapy imaging system primarily provides 2D X-ray projection images for patient positioning verification and 3D tomographic image reconstruction.Since multiple projection images acquired continuously exhibit strong spatiotemporal correlations,their similarities could be used to eliminate redundant information,thereby improving the image compression ratio.During image compression,the image sets obtained at different times were arranged into an image sequence which was input into a video encoder and output as a video file.During image decompression,the video file was input into a video decoder and output as an image sequence,and the images in the sequence were then assigned back to their original image sets.Three current dynamic video encoding algorithms(AVC,HEVC,and AV1)and the classic static image coding algorithm(JPEG 2000)were tested on a database of 2D projection images.The performance of various compression algorithms was evaluated using indicators such as compression ratio(CR),peak signal-to-noise ratio(PSNR),and structural similarity(SSIM).Moreover,visual comparison of projection images before and after compression was evaluated by clinical radiation oncologists.Results Dynamic video encoding algorithms achieved higher CR than the static image coding algorithm.The average CR of the 3 dynamic video encoding algorithms was as followed:CRAVC=11.50,CRHEVC=30.74,and CRAV1=27.10,while the average CR of the static image coding algorithm(JPEG 2000)was 5.28.For abdominal projection images,well-defined contours and textural details were preserved even when the CR reached 42.37.For head-neck projection images,although mild contour blurring emerged at a CR of 20.71,subsequent evaluation by clinical radiation oncologists confirmed that the reconstructed CBCT images still satisfied clinical requirements.Conclusion These dynamic video encoding algorithms effectively utilize the strong correlation information between multiple projection images,reduce the storage of redundant information,and greatly improve the image CR.

Objective To achieve efficient compression of on-board radiotherapy projection images using dynamic video encoding algorithms.Methods The on-board radiotherapy imaging system primarily provides 2D X-ray projection images for patient positioning verification and 3D tomographic image reconstruction.Since multiple projection images acquired continuously exhibit strong spatiotemporal correlations,their similarities could be used to eliminate redundant information,thereby improving the image compression ratio.During image compression,the image sets obtained at different times were arranged into an image sequence which was input into a video encoder and output as a video file.During image decompression,the video file was input into a video decoder and output as an image sequence,and the images in the sequence were then assigned back to their original image sets.Three current dynamic video encoding algorithms(AVC,HEVC,and AV1)and the classic static image coding algorithm(JPEG 2000)were tested on a database of 2D projection images.The performance of various compression algorithms was evaluated using indicators such as compression ratio(CR),peak signal-to-noise ratio(PSNR),and structural similarity(SSIM).Moreover,visual comparison of projection images before and after compression was evaluated by clinical radiation oncologists.Results Dynamic video encoding algorithms achieved higher CR than the static image coding algorithm.The average CR of the 3 dynamic video encoding algorithms was as followed:CRAVC=11.50,CRHEVC=30.74,and CRAV1=27.10,while the average CR of the static image coding algorithm(JPEG 2000)was 5.28.For abdominal projection images,well-defined contours and textural details were preserved even when the CR reached 42.37.For head-neck projection images,although mild contour blurring emerged at a CR of 20.71,subsequent evaluation by clinical radiation oncologists confirmed that the reconstructed CBCT images still satisfied clinical requirements.Conclusion These dynamic video encoding algorithms effectively utilize the strong correlation information between multiple projection images,reduce the storage of redundant information,and greatly improve the image CR.