由3D神经网络生成的CT通风图像显示了对基于雅可比和HU DIR的方法的改进,以预测量子化肺功能
Daryl Wilding-McBride1, Jeremy Lim2, Hilary Byrne2
1Medical Radiations, School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia.
Medical physics
|November 23, 2024
概括
一个新的3D神经网络从CT扫描中准确预测肺功能,通过识别高功能肺部区域来减少辐射肺炎风险,改善非小细胞肺癌 (NSCLC) 患者的放射治疗计划.
科学领域:
- 医学成像和放射瘤学医学成像和放射瘤学
- 医疗保健中的人工智能
- 计算生物学和生物信息学
背景情况:
- 辐射诱导的肺炎是非小细胞肺癌 (NSCLC) 患者的重大风险,发病率高达33%,死亡率为2%.
- 目前的放射治疗计划假定肺功能均,可能会辐射关键的高功能肺部区域.
- 通过节省高功能的肺部区域来避免功能性肺部,显示出降低肺炎风险的希望.
研究的目的:
- 开发一种更准确的方法来创建CT通风图像 (CTVI),以确定用于放射治疗计划的功能性肺部区域.
- 克服现有的CTVI方法的局限性,例如可变形图像注册 (DIR) 的不准确性和对文物的敏感性.
- 利用神经网络方法,直接从喘息CT (BHCT) 图像中预测通风图.
主要方法:
- 一个3D神经网络 (nnU-Net) 使用五倍交叉验证组合进行训练,从BHCT图像中预测通风图 (CTVInnU-Net).
- 培训数据包括20名患者的注册BHCT和GalligasPET图像,基准真相来自量化PET强度 (高,中,低功能).
- 性能与2D U-Net (CTVInnU-Net-2D) 和基于DIR的方法 (CTVIJac,CTVIHU) 进行了比较,评估了子相似系数 (DSC) 和豪斯多夫距离第95百分位数 (HD95).
主要成果:
- 三维神经网络 (CTVInnU-Net) 与地面真实性PET数据具有优越的相似性,平均DSC为0.68.
- 与其他方法相比,CTVInnU-Net显示了与功能区域 (HD95的22毫米) 的空间距离相似或更低.
- 3D神经网络在相似性 (DSC) 和空间精度 (HD95) 度量方面都超过了基于2D U-Net和DIR的方法.
结论:
- 开发的3D神经网络准确地产生量子化的CTVI,优于现有的2D U-Net和基于DIR的技术.
- 神经网络的直接CTVI预测消除了对后处理值的需求,以确定高功能肺部区域.
- 神经网络方法提供了更高的准确性和更快的评估,这表明在功能性肺避免策略中临床实施的巨大潜力.
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