使用双域神经网络,在配备带过器的形光束CT中提高图像质量
Sungho Yun1, Uijin Jeong1, Donghyeon Lee1
1Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Medical physics
|September 5, 2023
概括
这项研究引入了一种双域深度学习网络,以减少形光束CT (CBCT) 图像中的蝶结过器文物. 这种新的方法通过解决光束硬化和散射效应,显著提高了图像质量和软组织对比度.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 形光束CT (CBCT) 使用蝶结过器,导致空间不均的X射线束并导致日食文物.
- 患者分散进一步混这些文物,使它们依赖于患者和成像系统.
研究的目的:
- 提出一种新的双域网络,以有效减少CBCT图像中的蝶结过器诱导的文物.
- 为了增强软组织对比度,同时减轻图像工件.
主要方法:
- 开发了一个双域网络,其中有一个投影域组件来补偿波器诱导的光束硬化和一个图像域组件来减少与散射相关的文物.
- 投影域网络的输出被用于图像重建,然后在图像域网络中进行处理. 为了进行比较,还测试了一个单个图像域网.
主要成果:
- 拟议的双域网络显著减少了人工物,并在CBCT图像中增强了软组织对比度.
- 数字研究显示,感知损失平均下降了84.5%,RMSE下降了84.9%,SSIM增加了0.26.
- 实验研究表明,感知损失平均下降了87.2%,RMSE下降了92.1%,SSIM增加了0.58.
结论:
- 一个基于深度学习的双域框架有效地减少了蝶结过器文物,并改善了CBCT中的软组织对比度.
- 该方法的性能通过数值模拟和实验研究来验证,展示了其临床应用的潜力.
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