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从合成的x射线中实时抽取肺部提取改善了肺部图像引导放射治疗

Xinyi Fu¹, Katelyn Hasse¹, Di Xu¹

¹Department of Radiation Oncology, University of California, San Francisco, CA, United States of America.

Physics in medicine and biology

|January 8, 2025

概括

肺部瘤很难在X射线上看到. 一个新的AI网络,LeX-net,可以移除其他身体部位,以提高肺部可见性和瘤跟踪精度.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

在X射线中精确地定位肺瘤是具有挑战性的,因为隐蔽的可见性.
像肋骨和心脏这样的重叠的胸部结构阻碍了精确识别肺部异常.

研究的目的:

开发和验证一个肺部提取网络 (LeX-net) 以提高前后 (AP) X射线中的肺部显著性.
通过几乎去除重叠的胸部结构来提高图像引导程序中的肺瘤跟踪的准确性.

主要方法:

训练了一个具有条件GAN (LeX-net) 的Swin-UNet模型,使用来自73,965个3DCT和106个4DCT的数字重建放射图 (DRR) 来进行训练.
输入:全解剖DRRs;参考:从肺部提取的DRRs. LeX-net从全解剖DRR中生成了从肺部提取的DRR.
在4DCT衍生DRR上评估LeX-net性能,并使用模板匹配方法在4DCT测试集上评估瘤跟踪准确性.

主要成果:

在3DCT数据上,LeX-net在DRR (SSIM: 0.9581 ± 0.0151,PSNR: 30.78 ± 2.50) 中的肺部隔离方面取得了高性能.
当应用到4DCT数据时,性能略有下降,但仍然保持可用的肺部视图.
显著减少瘤跟踪误差 (0.97 ± 0.86毫米对比3.13 ± 5.82毫米) 和改善不同窗口大小的跟踪成功率.

结论:

LeX-net有效地去除重叠的解剖结构,增强X射线中的肺部可视化.
该模型从3DCT推广到4DCT衍生的DRR,证明了它的稳定性.
LeX-net显著改善了肺瘤跟踪结果,有助于图像引导干预.

关键词:

4DCT 4DCT是4DCT的直播方式.数字重建射线图 (DRR) 是指数字重建的射线图.生成性对抗性网络 (GAN) 是一种产生性对抗性网络.肺部的提取肺部的提取没有标记的瘤跟踪.

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