基于神经网络的粒子源剂量规划算法与临床应用初步应用研究
Optimization of Particle Source Dose Planning Algorithm Based on Neural Network
魏胜蓝 1孙亮1
作者信息
- 1. 苏州大学医学院放射医学与防护学院，苏州，215123
- 折叠
摘要
近距离放射性粒子植入治疗是恶性肿瘤精准放疗的重要手段。近年来，神经网络技术在医学影像分析和剂量预测领域展现出良好的应用潜力，但现有研究多集中于剂量分布预测，缺乏面向粒子布源与剂量优化的完整规划流程。针对上述问题，本文围绕粒子源自动布源与剂量优化，构建基于神经网络的智能剂量规划算法与临床应用优化流程。首先，分析粒子植入治疗的剂量计算机制及临床评价指标，建立粒子源剂量规划的物理与评价模型；其次，构建基于改进Attention U-Net的剂量预测模型，融合CT影像、靶区轮廓及粒子参数，实现三维剂量分布的高精度预测；随后，提出混合模拟退火与DVH评估的逆向布源算法，将预测剂量分布与临床剂量约束相结合，对粒子位置和数量进行优化；最后，构建完整的智能剂量规划流程，并通过临床病例数据对算法性能与应用效果进行验证。实验结果表明，靶区V???提升至92.8%，剂量均匀性指数HI降至1.05，危及器官剂量超标率从11.1%降至2.2%。单例患者推理时间小于1.18分钟，较传统手动规划缩短95%以上。在前列腺癌病例验证中，算法自动生成的计划满足V???≥92.8%、D??≥121.2Gy，所有危及器官剂量均在安全阈值内。本研究提出了一种面向临床应用的神经网络粒子源剂量规划方法，构建了完整的自动规划流程，能够有效提高剂量预测精度和规划效率，在保证靶区剂量覆盖的同时降低危及器官受照剂量，规划时间显著缩短，生成的剂量分布满足临床DVH评价指标要求，为近距离放疗智能化发展提供了新的技术路径，对提高治疗计划质量、降低规划成本、推动精准放疗规范化具有重要意义。
Abstract
Brachytherapy with radioactive seeds implantation is a crucial approach in precision radiotherapy for malignant tumors. In recent years, neural network technology has demonstrated remarkable potential in medical image analysis and dose prediction. However, existing research primarily focuses on predicting dose distribution, lacking a comprehensive planning process for seed placement and dose optimization. To address this issue, this paper centers on the automatic placement of seed sources and dose optimization, constructing an intelligent dose planning algorithm based on neural networks and optimizing clinical application processes. Firstly, analyze the dose calculation mechanism and clinical evaluation indicators of particle implantation therapy, and establish a physical and evaluation model for particle source dose planning. Secondly, construct a dose prediction model based on an improved Attention U-Net, integrating CT images, target contour, and particle parameters to achieve high-precision prediction of three-dimensional dose distribution. Subsequently, propose an inverse source placement algorithm combining simulated annealing and DVH evaluation, which combines predicted dose distribution with clinical dose constraints to optimize particle position and quantity. Finally, construct a complete intelligent dose planning process, and validate the algorithm performance and application effectiveness through clinical case data.
The experimental results showed that the target volume V100 increased to 92.8%, the dose homogeneity index HI decreased to 1.05, and the overdose rate of critical organs decreased from 11.1% to 2.2%. The reasoning time for a single patient was less than 1.18 minutes, which was more than 95% shorter than traditional manual planning. In the verification of prostate cancer cases, the plan automatically generated by the algorithm met the requirements of V100≥92.8% and D90≥121.2Gy, with all doses to critical organs within the safety threshold. This study proposes a neural network-based particle source dose planning method tailored for clinical applications, establishing a comprehensive automatic planning workflow. This approach effectively enhances the accuracy of dose prediction and planning efficiency, ensuring target dose coverage while reducing the radiation dose to critical organs. The planning time is significantly shortened, and the generated dose distribution meets the requirements of clinical Dose Volume Histogram (DVH) evaluation indicators. This provides a new technological path for the intelligent development of brachytherapy, and holds significant importance in improving the quality of treatment plans, reducing planning costs, and promoting the standardization of precision radiotherapy.关键词
神经网络/粒子源/剂量规划/近距离放射治疗/模拟退火Key words
neural network/particle source/dose planning/brachytherapy/simulated annealing引用本文复制引用
魏胜蓝,孙亮.基于神经网络的粒子源剂量规划算法与临床应用初步应用研究[EB/OL].(2026-11-12)[2026-11-13].http://www.paper.edu.cn/releasepaper/content/202611-3.学科分类
肿瘤学
