基于人体特征参数的CR系统摄影条件优化方法  

CR Condition Optimization Based on Physical Feature Parameters

作　　者：周玮[1] 王友政[1] 周占文[2] 曲睿[1] 

机构地区：[1]东北大学教育部流程工业综合自动化重点实验室,辽宁沈阳110004 [2]辽宁中医药大学附属医院医学影像中心,辽宁沈阳110032

出　　处：《东北大学学报（自然科学版）》2007年第5期631-634,647,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60574050)

摘　　要：用人工神经网络分析人体特征和计算机X射线摄影(CR)参数的定量关系,建立基于身体指数的CR摄影满意度下的人体特征参数、管电压与管电流.曝光时间和CR摄影得分为目标的0-1整数规划模型,用分组过滤方法简化解空间,最后用遗传算法求解.以拍摄胸片(PA)为对象的研究结果表明,CR摄影条件优化方法得到的结论与专家多年临床经验一致,人工神经网络和遗传算法可以在CR摄影中发挥作用,解决实际问题.该研究方法可以应用到CR系统对身体其他部位的摄影并推广到其他X射线摄影设备的应用之中.The quantitative relation between physical features and computerized radiography （CR） parameters is analyzed with artificial neural network to develop a 0-1 integer planning model based on physical feature parameters, tube voltage, tube current＇ exposure time and CR scoring according to the satisfiability of relevant photos for body indices. Grouped filtering method was used to simplify the solution space, then the optimized model is solved by genetic algorithm. The result of pleurography （PA） indicates that the conclusion drawn by this CR optimization method, is compatible with experts＇ long-time clinical experience and that both the artificial neural network and genetic algorithm play the role in CR and solving actual problems. In addition, this method can be used to analyze the CR photos of the rest of body, and extended to other X-ray photographic equipment.

关 键 词：X射线摄影 影像质量 照射剂量 身体指数 人工神经网络 遗传算法 

分 类 号：TP216[自动化与计算机技术—检测技术与自动化装置]