GDP-Net: Global Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal

Yikun Zhang;Guannan Liu;Yang Liu;Shipeng Xie;Jiabing Gu;Zujian Huang;Xu Ji;Tianling Lyu;Yan Xi;Shouping Zhu;Jian Yang;Yang Chen
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Abstract

In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on photon-counting detectors are affected by ring artifacts more severely. The flexibility and variety of detector responses make it difficult to build a well-defined model to characterize the ring artifacts. In this context, this study proposes the global dependency-enhanced dual-domain parallel neural network for Ring Artifact Removal (RAR). First, based on the fact that the features of ring artifacts are different in Cartesian and Polar coordinates, the parallel architecture is adopted to construct the deep neural network so that it can extract and exploit the latent features from different domains to improve the performance of ring artifact removal. Besides, the ring artifacts are globally relevant whether in Cartesian or Polar coordinate systems, but convolutional neural networks show inherent shortcomings in modeling long-range dependency. To tackle this problem, this study introduces the novel Mamba mechanism to achieve a global receptive field without incurring high computational complexity. It enables effective capture of the long-range dependency, thereby enhancing the model performance in image restoration and artifact reduction. The experiments on the simulated data validate the effectiveness of the dual-domain parallel neural network and the Mamba mechanism, and the results on two unseen real datasets demonstrate the promising performance of the proposed RAR algorithm in eliminating ring artifacts and recovering image details.
GDP-Net:环伪影去除的全局依赖增强双域并行网络
在计算机断层扫描(CT)成像中,由检测器响应不一致引起的环形伪影会严重降低重建图像的质量,对后续应用产生负面影响。基于光子计数探测器的新一代CT系统受到环伪影的影响更为严重。探测器响应的灵活性和多样性使得很难建立一个定义良好的模型来表征环伪制品。在此背景下,本文提出了基于全局依赖增强的双域并行神经网络环伪影去除(RAR)算法。首先,针对环伪信号在直角坐标系和极坐标下特征不同的特点,采用并行结构构建深度神经网络,提取和挖掘不同域的潜在特征,提高环伪信号去除的性能;此外,无论是在笛卡尔坐标系还是极坐标坐标系中,环伪影都是全局相关的,但卷积神经网络在建模远程依赖方面存在固有缺陷。为了解决这个问题,本研究引入了新的曼巴机制来实现一个全局接受场,而不会产生高的计算复杂性。它能够有效地捕获远程依赖关系,从而增强模型在图像恢复和减少伪影方面的性能。在仿真数据上的实验验证了双域并行神经网络和曼巴机制的有效性,在两个未见过的真实数据集上的实验结果证明了所提出的RAR算法在消除环伪影和恢复图像细节方面的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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