CT images corrupted by metal artifacts have serious negative effects on
clinical diagnosis. Considering the difficulty of collecting paired data with
ground truth in clinical settings, unsupervised methods for metal artifact
reduction are of high interest. However, it is difficult for previous
unsupervised methods to retain structural information from CT images while
handling the non-local characteristics of metal artifacts. To address these
challenges, we proposed a novel Dense Transformer based Enhanced Coding Network
(DTEC-Net) for unsupervised metal artifact reduction. Specifically, we
introduce a Hierarchical Disentangling Encoder, supported by the high-order
dense process, and transformer to obtain densely encoded sequences with
long-range correspondence. Then, we present a second-order disentanglement
method to improve the dense sequence's decoding process. Extensive experiments
and model discussions illustrate DTEC-Net's effectiveness, which outperforms
the previous state-of-the-art methods on a benchmark dataset, and greatly
reduces metal artifacts while restoring richer texture details.

我们提出了一种基于紧密变换增强编码网络（DTEC-Net）的无监督金属伪影削减方法，通过引入层次化解缠编码器、高阶紧密过程和变换器来处理 CT 图像中的非局部特征和保留结构信息。广泛的实验证明了 DTEC-Net 的有效性，在基准数据集上优于之前最先进的方法，并极大减少金属伪影同时还原丰富的纹理细节。