呼吸运动分析在肺癌放射治疗中的应用研究现状  

Research development of respiration motion analysis in radiotherapy of lung cancer

作　　者：李宝生[1] 王立英[1] 

机构地区：[1]山东省肿瘤医院放疗科,山东济南250117

出　　处：《中华肿瘤防治杂志》2006年第10期790-792,共3页Chinese Journal of Cancer Prevention and Treatment

摘　　要：呼吸运动对肺癌放射治疗的准确实施有重要影响,由于其运动幅度个体差异较大,需进行个体化的测量与分析。监测呼吸所导致的肿瘤运动主要包括X线透视、CT和MR等直接测量的方法,以及采用数学算法处理采集的图象,求解肿瘤的运动范围等间接的方法。事先预测靶区位置是保证示踪放疗技术准确实施的关键,目前的预测方法包括根据呼吸流量、胸腹部体表标记物的运动推算呼吸运动规律或建立肺部肿瘤运动的线性模型函数和应用线性预测模型、神经网络预测模型、Kalman滤波、自适应线形滤波和自适应神经网络算法等数学建模方法,发现应用预测模型特别是自适应神经网络算法减少了预测误差。但目前预测方法的准确性尚有待改进。Respiration motion has very important impacts on accurate implementation of lung cancer radiotherapy. Because of the big variation of lung motion magnitude among individual patients, patient specific measurement and analysis are needed. The methods of monitoring tumor motion induced by respiration include direct measuring with X-ray fluoroscopy, CT and MR, etc. and indirect method using some algorithms to do image processing and getting moving law of tumors. Predicting the future position of target is a key to ensure the accurate implementation of tracing radiotherapy. The most common predicting methods include models formed from airflow and surrogates on thoracic and abdomen, or lung moving linear model function of tidal volume and its temporal derive airflow, as well as mathematic models of linear predicting model, neural network model, Kalman filter, adaptive linear filter, and adaptive neural network, etc. The results show that the application of predicting models, especially the adaptive neural network algorithm, decreases the predicting error significantly. However, the accuracy of the current predicting methods is needed to improve further.

关 键 词：肺肿瘤/放射疗法 呼吸转运 综述文献 

分 类 号：R734.2[医药卫生—肿瘤]