Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that enables different forms of generations and regularizations between the artifact-affected and artifact-free image domains to support unsupervised learning. Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset. When applied to clinical datasets, our method achieves considerable improvements over the supervised models. The source code of this paper is publicly available at https://github. com/liaohaofu/adn.
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机译：基于深度神经网络的计算机断层扫描（CT）金属伪影（MAR）的方法是监督方法，其依赖于培训的合成数据。然而，随着合成数据可能无法完美地模拟CT成像的底层物理机制，监督方法通常概括为临床应用。为了解决这个问题，我们提出了据我们所知，第一个无人监督的学习方法。具体而言，我们介绍了一种新的工件解剖学网络，其能够在伪影和无伪图像的图像域之间实现不同形式的世代和正规化，以支持无监督的学习。广泛的实验表明，我们的方法显着优于现有的图像到图像翻译问题的无监督模型，并实现了对合成数据集的现有监督模型的可比性。当应用于临床数据集时，我们的方法通过监督模型实现了相当大的改进。本文的源代码在https：// github上公开使用。 Com / Liaohaofu / Adn。
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