TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction

• URL: http://arxiv.org/abs/2211.07190v1
• Date: Mon, 14 Nov 2022 08:28:57 GMT
• Title: TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction
• Authors: Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian, and Yanwei Qin
• Abstract summary: We propose a novel triple domain model-driven CTMAR network, termed as TriDoNet. We encode non-local repetitive streaking patterns of metal artifacts as an explicit tight frame sparse representation model with adaptive thresholds. Experimental results show that our TriDoNet can generate superior artifact-reduced CT images.
• Score: 7.959841510571622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR). However, most of them suffer from two limitations: (i) the domain knowledge is not fully embedded into the network training; (ii) metal artifacts lack effective representation models. The aforementioned limitations leave room for further performance improvement. Against these issues, we propose a novel triple domain model-driven CTMAR network, termed as TriDoNet, whose network training exploits triple domain knowledge, i.e., the knowledge of the sinogram, CT image, and metal artifact domains. Specifically, to explore the non-local repetitive streaking patterns of metal artifacts, we encode them as an explicit tight frame sparse representation model with adaptive thresholds. Furthermore, we design a contrastive regularization (CR) built upon contrastive learning to exploit clean CT images and metal-affected images as positive and negative samples, respectively. Experimental results show that our TriDoNet can generate superior artifact-reduced CT images.

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