Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.
While radiation treatment to patients with tumors in thorax and abdomen is being performed, further improvement of radiation accuracy is restricted by the tumor intra-fractional motion due to respiration. Real-time tumor tracking radiation is an optimal solution to tumor intra-fractional motion. A review of the progress of real-time dynamic multi-leaf collimator (DMLC) tracking is provided in the present review, including DMLC tracking method, time lag of DMLC tracking system, and dosimetric verification.
【摘要】 目的 研究Monaco治療計劃系統中不同參數設置對容積旋轉調強放射治療(VMAT)計劃質量的影響，得出更合理的治療計劃參數設置以提高VMAT治療質量。 方法 2010年1-5月間治療3例患者，為食管癌、宮頸癌和鼻咽癌各1例，分別設置不同的計劃參數進行容積旋轉調強計劃優化，通過多種評估指標比較各VMAT計劃質量的差異，得出臨床所需的MSC、MSS、SSF、Sm、MMS和MDR共6個治療計劃參數對VMAT治療質量的影響。 結果 MSC、MSS和SSF的3個參數對VMAT治療質量不產生影響，有影響的Sm、MMS和MDR參數中，隨著Sm和MMS值的增大，VMAT計劃的劑量分布逐漸變差，但控制點數、機器跳數和照射時間均逐漸減小；隨著MDR值增大，VMAT治療的劑量分布先逐漸變差后不變，控制點數和機器跳數均是先增大后不變，而照射時間是先減小后不變。 結論 Sm、MMS和MDR 3個參數對VMAT計劃質量有較大影響，對不同的患者，設置合適的Sm、MMS和MDR值對提高計劃質量非常重要。【Abstract】 Objective To investigate the impacts of parameter settings on the quality of plans for the volumetric modulated arc therapy (VMAT) with Monaco treatment planning system. Methods Three patients who underwent VMAT from January to May 2010 were selected. The planning optimizations were processed by setting different planning parameters, including MSC, MSS, SSF, Sm, MMS and MDR, respectively. Then the quality of each plan with a certain set of parameters was evaluated by various evaluation indexes. The differences of quality among different plans were analyzed by comparing these indexes. Results There was no influence on the quality of VMAT planning for the parameter MSC, MSS and SSF to be set with different values. However, the other three parameters, MSC, MSS and SSF , affected the quality of VMAT planning with different values. Along with the aggrandizement of Sm and MMS value, the dose distribution of VMAT plans gradually became bad, while the number of control points, machine monitor units and irradiation time were gradually reduced. And along with the aggrandizement of MDR value, the dose distribution of VMAT plans became bad gradually until a constant state was reached, and both the number of control points and machine monitor units increased at first and then kept constant, while irradiation time decreased at first and then kept constant. Conclusion The selections of parameter Sm, MMS and MDR impact the quality of VMAT planning greatly. It is very important to set the suitable value of Sm, MMS and MDR to get the best planning quality for patients with different complexity.
Patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) process is an important component of the implementation process of clinical radiotherapy. The tolerance limit and action limit of discrepancies between the calculated dose and the delivered radiation dose are the key parts of the VMAT QA processes as recognized by the AAPM TG-218 report, however, there is no unified standard for these two values among radiotherapy centers. In this study, based on the operational recommendations given in the AAPM TG-218 report, treatment site-specific tolerance limits and action limits of gamma pass rate in VMAT QA processes when using ArcCHECK for dose verification were established by statistical process control (SPC) methodology. The tolerance limit and action limit were calculated based on the first 25 in-control VMAT QA for each site. The individual control charts were drawn to continuously monitor the VMAT QA process with 287 VMAT plans and analyze the causes of VMAT QA out of control. The tolerance limits for brain, head and neck, abdomen and pelvic VMAT QA processes were 94.56%, 94.68%, 94.34%, and 92.97%, respectively, and the action limits were 93.82%, 92.54%, 93.23%, and 90.29%, respectively. Except for pelvic, the tolerance limits for the brain, head and neck, and abdomen were close to the universal tolerance limit of TG-218 (95%), and the action limits for all sites were higher than the universal action limit of TG-218 (90%). The out-of-control VMAT QAs were detected by the individual control chart, including one case of head and neck, two of the abdomen and two of the pelvic site. Four of them were affected by the setup error, and one was affected by the calibration of ArcCHECK. The results show that the SPC methodology can effectively monitor the IMRT/VMAT QA processes. Setting treatment site-specific tolerance limits is helpful to investigate the cause of out-of-control VMAT QA.
【摘要】 目的 研究千伏級錐形束CT（kV-cone beam CT，kV-CBCT）影像用于鼻咽癌調強放射治療計劃劑量計算的可行性和精確度。 方法 2010年7-9月7例鼻咽癌患者 ，獲取每例患者的第1天放射治療時的kV-CBCT影像。用CIRS062密度模體和患者自身特定區域亨氏單位值（hounsfield unit，HU）映射的兩種方法重新刻度亨氏單位值-相對電子密度（HU-RED）表，分別進行劑量計算，并與在傳統扇形束CT（FBCT）影像上的原放射治療計劃結果進行對比，包括輻射劑量分布、靶區和危及器官的劑量體積直方圖（DVH）。 結果 kV-CBCT影像的治療計劃和原治療計劃在劑量分布和DVH上有較好的一致性。在劑量分布的比較上采用了γ分析（2%/2 mm標準的通過率），用基于模體的HU-RED表得到的治療計劃與原治療計劃對比，在經過等中心冠狀面、矢狀面和橫斷面的通過率分別為92.7%±3.5%、95.1%±3.1%和95.7%±3.4%，用基于患者的HU-RED表得到治療計劃與原治療計劃對比的通過率分別為94.8%±2.7%、96.6%±2.9%和97.4%±2.7%。DVH的統計數據表明，兩種方法得到的kV-CBCT治療計劃和原治療計劃相比較，靶區和危及器官劑量偏差大多數在2%以內。有1例因在橫斷面發生了明顯的旋轉誤差，導致在橫斷面的通過率很低，DVH統計數據較原計劃偏差較大。 結論 kV-CBCT影像可以用來做輻射劑量計算，基于患者自身影像生成的HU-RED表的治療計劃較原治療計劃有更高的符合度。【Abstract】 Objective To evaluate the feasibility and accuracy of dose calculation based on cone beam CT (CBCT) data sets for intensity modulated radiation therapy (IMRT) planning of nasopharyngeal cancer (NPC). Methods Seven NPC patients were selected. The kV-CBCT images for each patient were acquired on the first treatment day. Two correction strategies were used to generate the cone beam HU value vs relative electron density calibration tables which named CIRS062 phantom based HU-RED tables and patient specific HU-RED tables respectively for dose calculation. The dose distributions and dose volume histograms (DVHs) of the target and organs at risk (OAR) based on kV-CBCT images were compared to the plans based on the fan-beam CT (FBCT). Results The DVH and dose distribution comparison between plans based on the FBCT and those on the CBCT showed good agreements. The γ analysis with a criterion of 2 mm/2% was used for the comparison of dose distribution at the coronal plane, sagital plane and cross plane through the isocenter point. The passing rate from phantom based HU-RED tables were (92.7±3.5) %, (95.1±3.1) %, and (95.7±3.4)%, respectively. The passing rates from the patient specific HU-RED tables were (94.8±2.7) %, (96.6±2.9) %, and (97.4±2.7) %, respectively. The dose difference between plans based on CBCT and those based on FBCT was within 2% at most patients by analyzing DVH based parameters. Only one patient who had significant rotation setup error resulted in the low passing rate and disagreement in DVH. Conclusion The CBCT images can be used to do dose calculation in IMRT planning of NPC. The differences between plans based on HU-RED tables generated by specific patient and the original plans are less than those between plans based on CIRS062 phantom based HU-RED tables and the original plans.
ObjectiveTo systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy (RT), explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. MethodsA comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes and organs at risk, intelligent treatment planning, and prediction of RT-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. ResultsAI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporated multimodal data enabled more accurate, personalized risk assessment, supporting safer and more effective individualized RT. ConclusionsRT is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of RT, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in RT.