InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

Abstract

During the computed tomography (CT) imaging process, metallic implants within patients always 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 every network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel interpretable 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 https://github.com/hongwang01/InDuDoNet_plus.

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

“…ProxNet: Following recent great deep unfolding-based works [Wang et al, 2021b;Xie et al, 2019], we also set proximal operator with ResNet. Although it is very hard to inversely derive the form of regularizer by integrating ResNet function due to its complicated form, but as has comprehensively substantiated by previous research, it does be helpful to explore insightful structural prior.…”

“…Nevertheless, common metallic implants within patients, such as dental fillings and hip prosthesis, would adversely cause the missing of projection data during CT imaging, and thus lead to the obvious streaking artifacts and shadings in the reconstructed CT images. A robust † Corresponding author model, which automatically reduces the unsatisfactory metal artifacts and improves the quality of CT images for subsequent clinical treatment, is worthwhile to develop [Wang et al, 2021b;Wang et al, 2021a;.…”

“…Later, researchers adopted different learning strategies, e.g., residual learning [Huang et al, 2018] and adversarial learning , to directly learn the artifact-reduced CT images. Very recently, there is a new research line for the MAR task, which focuses on the mutual learning of sinograms and CT images Lyu et al, 2020;Wang et al, 2021b;Wang et al, 2021c].…”

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

“…Nevertheless, common metallic implants within patients, such as dental fillings and hip prosthesis, would adversely cause the missing of projection data during CT imaging, and thus lead to the obvious streaking artifacts and shadings in the reconstructed CT images. A robust † Corresponding author model, which automatically reduces the unsatisfactory metal artifacts and improves the quality of CT images for subsequent clinical treatment, is worthwhile to develop [Wang et al, 2021b;Wang et al, 2021a;Lin et al, 2019].…”

“…Later, researchers adopted different learning strategies, e.g., residual learning [Huang et al, 2018] and adversarial learning , to directly learn the artifact-reduced CT images. Very recently, there is a new research line for the MAR task, which focuses on the mutual learning of sinograms and CT images [Lin et al, 2019;Lyu et al, 2020;Wang et al, 2021b;Wang et al, 2021c].…”