用合成数据训练网络，同时修正CT图像和 sinogram 中的坏像素问题。
Low performing pixel correction in computed tomography with unrolled network and synthetic data training
- 基于合成数据构建可展开的双域网络，利用CT成像几何关联性。
- 在1%-2%探测器缺陷下，性能显著优于现有方法。
- 无需真实临床数据，适配多种扫描仪，适合软件部署。
CT探测器中的低性能像素（LPP）会导致重建图像出现环状和条纹伪影，使其无法临床使用。近年来虽有基于监督深度学习的方法在图像域或sinogram域进行修复，但均需昂贵的真实数据集训练。且现有方法仅关注单一域，忽略CT几何正向过程中的内在关联。本文提出一种基于合成数据的可展开双域方法，通过自然图像生成的合成数据，利用LPP在sinogram与图像域间的内在关联，使模型无需真实临床数据即可有效修复伪影。在模拟中心区域1%-2%探测器缺陷的实验中，该方法显著超越当前最优方案。结果表明，该方法可避免数据采集成本，且适用于不同扫描仪设置，适合软件化应用。
原文摘要 · Abstract (English)
Low performance pixels (LPP) in Computed Tomography (CT) detectors would lead to ring and streak artifacts in the reconstructed images, making them clinically unusable. In recent years, several solutions have been proposed to correct LPP artifacts, either in the image domain or in the sinogram domain using supervised deep learning methods. However, these methods require dedicated datasets for training, which are expensive to collect. Moreover, existing approaches focus solely either on image-space or sinogram-space correction, ignoring the intrinsic correlations from the forward operation of the CT geometry. In this work, we propose an unrolled dual-domain method based on synthetic data to correct LPP artifacts. Specifically, the intrinsic correlations of LPP between the sinogram and image domains are leveraged through synthetic data generated from natural images, enabling the trained model to correct artifacts without requiring any real-world clinical data. In experiments simulating 1-2% detectors defect near the isocenter, the proposed method outperformed the state-of-the-art approaches by a large margin. The results indicate that our solution can correct LPP artifacts without the cost of data collection for model training, and it is adaptable to different scanner settings for software-based applications.
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