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.

翻译：受金属伪影污染的CT图像对临床诊断具有严重的负面影响。考虑到临床场景中难以收集带有真实标签的配对数据，无监督金属伪影减少方法备受关注。然而，现有无监督方法难以在保留CT图像结构信息的同时处理金属伪影的非局部特性。为解决这些挑战，我们提出了一种新颖的基于密集Transformer的增强编码网络（DTEC-Net），用于无监督金属伪影减少。具体而言，我们引入了由高阶密集过程与Transformer支持的分层解缠编码器，以获取具有远程对应关系的密集编码序列。随后，我们提出了一种二阶解缠方法，以改进密集序列的解码过程。大量实验与模型讨论表明，DTEC-Net在基准数据集上优于先前最先进方法，有效减少了金属伪影，同时恢复了更丰富的纹理细节。