基于EPID的In vivo剂量测量深度学习模型的剂量特征
Qilin Li1,2,3, Dingshu Tian1,2, Guangyao Sun4,5
1Institute of Nuclear Energy Safety Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
这项研究开发了一种用于二维EPID剂量计的CycleGAN模型,将电子门户成像设备图像转换为准确的剂量图. 适当的规范化显著提高了辐射疗法质量保证模型的准确性.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 机器学习 机器学习
背景情况:
- 电子门户成像设备 (EPID) 在图像中捕获剂量信息.
- 这些信息可以转换为用于剂量测量的2D剂量图.
- 基于CycleGAN的模型被开发用于这种2D EPID剂量测量应用.
研究的目的:
- 开发和评估基于CycleGAN的2DEPID剂量计模型.
- 评估开发模型的剂量特征和准确性.
- 调查不同规范化方法对模型性能的影响.
主要方法:
- 测量是在用EPID探测器在Linac上进行的.
- 用治疗计划系统计算剂量分配作为基本事实.
- 循环GAN模型使用两个规范化方法将EPID图像转换为2D剂量图.
- 马分析和剂量线性用于模型评估.
主要成果:
- EPID剂量特征显示出高精度,线性观察到超出12厘米幻影厚度.
- 循环GAN模型有效地将EPID图像转换为平面剂量图.
- 规范化方法II在马分析中实现了97.9%的平均通过率 (3mm,3%),显著优于方法I (85.5%).
结论:
- EPID是捕获剂量信息的宝贵工具,可以使用CycleGAN模型准确地转换为平面剂量图.
- 开发的模型显示了辐射疗法治疗计划质量保证的潜力.
- 选择适当的规范化方法对于减轻剂量非线性和提高准确性至关重要.
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