【作者】 王卓宇；

【导师】 周凌宏；

【作者基本信息】 第一军医大学 ， 生物医学工程， 2006， 硕士

【摘要】 放射治疗是医治癌症的三大主要手段之一，据估计，约有60％-70％的肿瘤病人需要进行放射治疗，40％的癌症治愈病人是由放射治疗治愈的，具有十分重大的意义。从经典的三维适形放射治疗技术发展到现在的调强放射治疗技术(intensity-modulated radiotherapy，IMRT)，是放射肿瘤学史上的一次重大变革。但鉴于实际问题的复杂性，IMRT的优势还远没有在临床应用中完全发挥出来。IMRT治疗计划作为调强放疗的核心和基础，其中尚有许多急需解决的问题。 随着IMRT研究的深入和逆向治疗计划的发展，放射治疗中如何自动选择射野参数引起了广泛关注。在传统放射治疗中，射野参数的选择是通过反复试错(try and error)的方法实现的，治疗计划的优劣往往依赖于计划设计者的经验，由于IMRT采用逆向计划，需要选择的参数很多，通过试错的方式根本无法完成，必须使用优化方法来提高治疗计划设计水平。在过去的十多年中，人们对IMRT射野参数优化的优化方法进行了大量研究，最常用的包括：线性规划法、均方优化法、梯度方法、有约束模拟退火法和遗传算法等。 梯度算法是目前商用调强放疗(IMRT)计划系统中最常用的算法之一。M(?)ller提出的SCG(scaled conjugate gradient，缩放共轭梯度法)算法很好地解决了梯度算法中的线性搜索过程，进一步提高了梯度算法的性能。X.D.Zhang等人在Philips的Pinnacle计划系统上证实，缩放共轭梯度法的速度是常规共轭梯度法的更多还原

【Abstract】 Radiotherapy, one of the three main treatments for tumor, is treat with about 60%-70% patients suffering from cancer and cures 40% of them. It is a historic advancement that the classical three-dimensional (3D) conformal radiotherapy (3DCRT) evolved into the intensity-modulated radiotherapy (IMRT). However, the advantages of IMRT have not been fully utilized yet, due to the complicated clinical conditions. There are still many problems in the IMRT planning, which is one of the basics of IMRT application, should be solved.With the development of IMRT and inverse planning , the automatic determination of suitable beam parameters in external beam radiotherapy has gained wide interests. In the conventional radiotherapy, the beam parameters are usually obtained by trial and error. The planning outcomes usually depend on the personal experience of planners. But there are large-scale parameters to be determined in the inverse planning of IMRT. It can hardly be carried out by trial and error. During last ten years, many methods have been proposed to optimize treatment plans such as linear programming, quadratic programming, gradient algorithms, constrained simulated annealing and genetic algorithms.更多还原