深度学习模型的比较,用于在Varian Halcyon上构建二维非过境EPID剂量计
Muhammad Mahdi Ramadhan1, Wahyu Edy Wibowo2, Prawito Prajitno1
1Department Physics, Faculty of Mathematics and Natural Sciences Universitas Indonesia, Depok, Indonesia.
概括
这项研究评估了使用电子门户成像设备 (EPID) 进行非过境剂量测量的深度学习模型. 一个模型显著提高了图像相似性,显示了放射治疗应用的前景.
科学领域:
- 医学物理 医学物理
- 放射疗法是一种放射治疗.
- 人工智能的人工智能
背景情况:
- 深度学习模型在瘤学和放射治疗中越来越多地用于预测和决策.
- 非过渡剂量测量对于准确的辐射剂量传递至关重要.
- 电子门户成像设备 (EPID) 用于放射治疗中的质量保证.
研究的目的:
- 为了比较五种深度学习模型在构建非传输剂量计中的有效性.
- 评估EPID图像和治疗计划系统 (TPS) 生成的剂量图像之间的相似性.
- 通过使用Varian Halcyon和a-Si EPID验证放射治疗应用的深度学习模型.
主要方法:
- 利用47个乳腺癌患者计划作为Eclipse TPS的基本真相.
- 在Varian Halcyon上照射了a-Si 1200 EPID探测器.
- 增强和随机分割的EPID和TPS图像用于培训和验证.
- 创建并验证了使用3%/3毫米马指数的五种深度学习模型.
主要成果:
- 在五个深度学习模型中,有四个成功地增强了EPID和TPS剂量图像之间的相似性.
- 模型A实现了最高的平均马传递率,达到90.07±4.96%.
- 其他模型 (B,C,D,E) 显示平均马传导率在77.42%至80.47%之间.
结论:
- 深度学习模型可以快速增加EPID和TPS图像相似性,用于非过境剂量测量.
- 在广泛的临床采用之前,需要对更多临床病例进行进一步验证.
- 这项研究表明,人工智能在提高放射治疗剂量计准确度方面的潜力.
相关概念视频
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