TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction
This work addresses metal artifact reduction in CT imaging for medical applications, representing an incremental improvement over existing deep learning methods.
The authors tackled CT metal artifact reduction by proposing TriDoNet, a network that embeds triple domain knowledge and uses a sparse representation model and contrastive regularization, resulting in superior artifact-reduced CT images as shown in experiments.
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.