一种深度学习方法,从对比增强CT中去除对比度,用于质子剂量计算
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.
Journal of applied clinical medical physics
|January 25, 2024
概括
一种新的深度学习方法从对比度增强的CT扫描 (CECT) 中生成非对比度增强的CT (NCECT). 这种方法减少了由组织运动引起的质子剂量计算中的不确定性,提高了治疗的准确性.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 质子剂量计算通常需要非对比增强CT (NCECT),而对比增强CT (CECT) 用于瘤划分.
- 在NCECT和CECT扫描之间组织运动引入了剂量测量不确定性,特别是在胸部和腹部移动瘤中.
研究的目的:
- 开发和评估一种深度学习方法,直接从CECT生成NCECT.
- 为了减少CT模拟时间,患者成像剂量和质子治疗中的运动诱导的不确定性.
主要方法:
- 训练了一个深度神经网络,将3D CECT图像转换为去掉对比度的NCECT图像补丁.
- 该模型经过训练和测试,使用来自20名患者的腹部CECT/NCECT数据的8000个图像补丁对在可变形注册后进行测试.
- 使用临床质子疗法计划和患者数据评估了剂量计影响.
主要成果:
- 深度学习模型实现了高准确性,共弦相似度为0.988和MSE为0.002.
- 与CECT相比,生成的NCECT显著降低了平均HU差异 (∼4.72比∼64.52),改善了~93%.
- 使用生成的NCECTs进行的质子剂量计算显示PTV (3.5%) 和GTV (5.5%) V100%与CET相比变化很小,但突出显示了远距离光束路径的剂量差异.
结论:
- 一种深度学习方法有效地从CECT中生成NCECT.
- 这种技术可以减轻因扫描间组织运动而产生的质子剂量计算中的不确定性,从而提高治疗计划的准确性.
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