  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.
