论文标题
双能Energy CT的材料分解方法通过双重交互式WASSERSTEIN生成对抗网络
A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks
论文作者
论文摘要
双能计算机断层扫描在材料表征和识别方面具有巨大的潜力，而重建的特定于材料的图像始终遭受放大的噪声和光束硬化的伪影。在这项研究中，提出了一种数据驱动的方法，该方法使用双重交互式瓦斯汀生成对抗网络提出了提高材料分解准确性。具体而言，使用两个交互式生成器来合成相应的材料图像，并为训练的不同损失函数合并了分解模型以保留生成的图像中的纹理和边缘。此外，还采用选择器来确保两个发电机的建模能力。模拟幻象和实际数据的结果证明了这种方法在抑制噪声和横梁硬化伪影方面的优势。
Dual-energy computed tomography has great potential in material characterization and identification, whereas the reconstructed material-specific images always suffer from magnified noise and beam hardening artifacts. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks is proposed to improve the material decomposition accuracy. Specifically, two interactive generators are used to synthesize the corresponding material images and different loss functions for training the decomposition model are incorporated to preserve texture and edges in the generated images. Besides, a selector is employed to ensure the modelling ability of two generators. The results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing the noise and beam hardening artifacts.
