基于共轭梯度法的调强放疗射束强度分布优化
Beam Intensity Map Optimization Based on Conjugate Gradient in Intensity Modulated Radiation Treatment
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摘要: 针对逆向调强放疗中强度分布优化涉及的参数多， 且临床上对其优化速度要求高的特点， 将医生期望的靶区剂量和周围正常组织剂量限制转化为二次函数形式的目标函数， 然后利用共轭梯度法对该目标函数进行优化。 最后采用一例C形靶区紧密包围危及器官的模拟病例和一例临床常用的前列腺实例， 在PC机（CPU E7200@2.53GHz， 2.00GB内存， Windows XP）上对强度分布优化效果进行测试， 对模拟病例10 s便找到最优解； 而对前列腺病例20 s便可以找到最优解； 且两个测试病例优化所得强度分布对应的剂量分布均满足要求。 测试结果表明， 采用共轭梯度法优化强度分布具有快速和效果好的优点， 因此可以将其应用在精确放疗系统中。
The beam intensity map optimization of Intensity Modulated Radiation Treatment(IMRT) is a large scale optimization problem because of thousands of parameters involved. A fast and efficient approach was studied in the paper according to the clinical requirement for high speed and good results. Firstly, the clinical prescribed dose of Planning Target Volume(PTV) and dosevolume constraints of Normal Tissue and Organ at Risk(OAR) were transformed into a quadratic objective function. And then Conjugate Gradient(CG) was adopted to optimize the objective function. At last, a simulated case and a clinical case were used to test the approach. The results showed that the optimization process need 40 s while satisfied results could be obtained in 10 s for simulated case and the optimization process need 1 min and 20 s while optimized results could be obtained in 20 s for the clinical prostate case. So it can be found that the approach of proposed in this paper is valid and efficient, and can be used to the accurate radiation therapy system.Abstract: The beam intensity map optimization of Intensity Modulated Radiation Treatment(IMRT) is a large scale optimization problem because of thousands of parameters involved. A fast and efficient approach was studied in the paper according to the clinical requirement for high speed and good results. Firstly, the clinical prescribed dose of Planning Target Volume(PTV) and dosevolume constraints of Normal Tissue and Organ at Risk(OAR) were transformed into a quadratic objective function. And then Conjugate Gradient(CG) was adopted to optimize the objective function. At last, a simulated case and a clinical case were used to test the approach. The results showed that the optimization process need 40 s while satisfied results could be obtained in 10 s for simulated case and the optimization process need 1 min and 20 s while optimized results could be obtained in 20 s for the clinical prostate case. So it can be found that the approach of proposed in this paper is valid and efficient, and can be used to the accurate radiation therapy system.
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