The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the...

中文翻译：

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使用混合域卷积神经网络进行伪影去除以进行有限角度CT成像

用有限角度配置来抑制计算机断层摄影中的条纹伪影是具有挑战性的。常规的分析算法，例如滤波反投影（FBP），由于投影数据不完整而无法成功执行。此外，基于模型的迭代总变化算法可有效减少小条纹，但在消除大条纹方面效果不佳。相比之下，FBP映射网络和基于深度学习的后处理网络在消除较大的条纹伪像方面表现出色。但是，这些方法在单独的域中执行处理，并且尚未同时探索在不同域中运行的多种深度学习算法的优势。在本文中，我们提出了一种混合域卷积神经网络（hdNet），用于减少有限角度计算机断层扫描中的条纹伪影。