一个生成的对抗网络,以改进集成模式的质子成像分辨率,使用对联的质子-碳数据
Mikaël Simard1, Ryan Fullarton1, Lennart Volz2
1Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Medical physics
|September 10, 2025
概括
深度学习通过将质子图像转换为碳离子数据来提高质子放射学 (pRad) 的分辨率. 这种方法可以提高临床应用的图像质量,使用对对的质子-碳数据集.
科学领域:
- 医疗成像医学成像
- 粒子物理学 粒子物理学
- 人工智能的人工智能
背景情况:
- 质子放射学 (pRad) 提供了临床可访问性,但由于多重库伦散射 (MCS) 导致空间分辨率有限.
- 像碳离子这样的更重离子表现出减少的MCS,从而导致本质上具有更高分辨率的放射图 (cRads).
- 通过图像翻译网络将质子数据转换为相当的碳离子数据,可以实现对pRads的图像分辨率增强.
研究的目的:
- 开发和评估一个深度学习框架,以提高集成模式质子X射线图的空间分辨率.
- 为了利用配对的质子-碳成像数据来实现高准确度的图像翻译.
主要方法:
- 一个有条件的生成对抗网络,称为Proton2Carbon,被开发用于将质子笔束图像转换为合成碳离子束图像.
- 该模型是在使用闪光探测器获得的547,224对质子-碳图像的大数据集上进行训练的.
- 评估包括在内部和外部数据集上使用自定义的3D打印线对模块来评估空间分辨率.
主要成果:
- Proton2Carbon模型成功地提高了pRad的空间分辨率,从1.7到2.7 lp/cm (内部) 和2.3 lp/cm (外部),证明了通用性.
- 保持了水相当厚度的准确性,与pRads和cRads相比.
- 幻影研究显示,翻译图像的结构清晰度提高,尽管观察到的噪声略有增加.
结论:
- 深度学习通过利用对对的质子-碳数据有效地提高了质子放射学图像质量.
- Proton2Carbon框架显示了将其集成到临床成像工作流程中的潜力,改善了质子放射学应用.
- 培训数据集是公开发布的,以鼓励进一步的研究在这个领域.
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