运动和解剖学双意识肺透气成像通过整合雅可比图和平均CT图像使用双路径融合网络
Pei Ma1, Zhi Chen1, Yu-Hua Huang1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, Hong Kong.
Medical physics
|October 21, 2024
概括
一种新的双意识计算机断层扫描 (CT) 通风成像 (CTVI) 方法通过整合解剖和运动数据来增强肺通风估计. 这种方法提高了功能性肺避开放射治疗 (FLART) 的准确性,特别是在有阻塞的患者中.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 人工智能在医学中的应用
背景情况:
- 基于深度学习的CTVI显示出指导功能性肺回避放射治疗 (FLART) 的前景.
- 仅使用解剖学CT数据的传统CTVI方法可能会因为缺乏运动数据集成而错过关键的通风功能.
研究的目的:
- 开发一种新的双意识CTVI方法,整合解剖CT数据和雅可比图运动数据.
- 为了生成更准确的通风图像,以改善FLART指导.
主要方法:
- 使用66名患者的4DCT图像开发了一种双路融合网络 (DPFN).
- DPFN将雅可比克地图的运动数据与平均CT图像的解剖数据整合在一起,使用专门的双路径.
- 通过将合成的CTVI (CTVI双) 与参考通风图像 (RefVI) 进行比较,使用相关系数和Dice相似系数来评估性能.
主要成果:
- CTVI双实现了0.70的Spearman's R平均值,明显优于其他CTVI方法 (p <0.05).
- 高和低功能区域的子相似系数分别为0.64和0.80,超过了CTVISVD和CTVIUnet.
- 与基于双相CT,放射学,超级voxel,Unet和可变形注册方法相比,CTVI双显示出更高的性能.
结论:
- 一种新的双意识CTVI模型通过整合解剖和运动信息,有效地合成肺透气图像.
- 纳入运动数据显著提高了肺透气估计的准确性,特别是对于瘤诱导阻塞的患者.
- 这种改进的CTVI准确性有可能使FLART交付更有效.
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