通过使用生成对抗网络 (GAN) 风格转换来减少CT的供应商差异,改善间歇性肺病量化的功能相关性
Jooae Choe1, Hye Jeon Hwang1, Min Seon Kim2
1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
European journal of radiology
|December 31, 2024
概括
生成对抗性网络 (GAN) 改善了供应商之间的CT图像转换,增强了异常性肺纤维化 (IPF) 的量化和与肺功能测试的相关性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 肺部医学 肺部医学
背景情况:
- 使用定量CT (QCT) 量化异常性肺纤维化 (IPF) 的量化取决于供应商.
- 深度学习软件用于ILD量化通常是针对特定CT供应商数据进行训练的.
- CT采集的变化可能会影响ILD测量的准确性.
研究的目的:
- 评估可路由生成对抗网络 (RouteGAN) 在供应商之间用于CT风格转换的有效性.
- 为了确定基于GAN的CT风格转换是否能最大限度地减少ILD量化中的变化.
- 评估改进的QCT测量是否与风格转换后的肺功能测试 (PFT) 更好地相关.
主要方法:
- 对112名IPF患者的胸部CT进行了回顾性分析,这些患者与供应商A.
- 来自供应商A的CT图像使用RouteGAN生成供应商B风格的CT图像.
- 在原始和转换的图像上量化ILD特征 (网状,蜂状,磨砂玻璃不透明).
- 放射学家使用一致性相关系数 (CCC) 和视觉准确度评估测量变异性.
- 评估QCT参数与PFT指标之间的相关性.
主要成果:
- 路线GAN在蜂 (CCC=0.72) 和地面玻璃不透明度 (CCC=0.59) 上取得了中等到良好的协议,但在网状化 (CCC=0.20) 上取得了较低的协议.
- 与原始图像相比,使用转换图像进行量化时,视觉准确度得分明显高 (P < 0.001).
- CT风格转换显著改善了纤维化得分和PFT测量之间的相关性,包括强迫生命能力和肺部对一氧化碳的扩散能力.
结论:
- 基于GAN的CT风格转换提高了基于深度学习的ILD量化准确度.
- 在风格转换后,QCT精度的提高导致了IPF患者测量的更好的功能相关性.
- 这种技术显示了在不同CT供应商之间标准化ILD量化方面的希望.
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