InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images
This addresses harmful artifacts from metallic implants in CT scans, which degrade image quality and affect clinical diagnosis, representing an incremental improvement with enhanced interpretability.
The paper tackled metal artifact reduction in CT images by proposing InDuDoNet+, a deep unfolding dual domain network that embeds CT imaging geometry and improves interpretability, achieving superior generalization performance beyond state-of-the-art methods on synthesized and clinical data.
During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific MAR task; hence, the role of each network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, we analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. . Code is available at \url{https://github.com/hongwang01/InDuDoNet_plus}.