Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Wei Cui, Haipeng Lv, Jiping Wang, Yanyan Zheng, Zhongyi Wu, Hui Zhao, Jian Zheng, Ming Li
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引用次数: 0

Abstract

Background: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT.

Objective: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images.

Methods: Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details.

Results: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods.

Conclusions: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

使用互补学习的特征共享多解码器网络,用于抑制光子计数 CT 环形伪影。
背景:光子计数计算机断层扫描(Photon-counting CT)利用光子计数探测器对入射光子进行精确计数并测量其能量。与传统的能量积分探测器相比,这些探测器能提供更好的图像对比度和材料区分度。然而,与传统的螺旋 CT 不同,光子计数 CT 由于光子计数有限和探测器响应变化,往往会出现更明显的环状伪影:为了全面解决这一问题,我们提出了一种新颖的特征共享多解码器网络(FSMDN),利用互补学习来抑制光子计数 CT 图像中的环状伪影:具体来说,我们采用特征共享编码器来提取上下文和环状伪影特征,从而促进有效的特征共享。这些共享特征还可由专用于上下文和环状伪影通道的独立解码器并行处理。通过互补学习,这种方法在保留组织细节的同时,在伪影抑制方面实现了卓越的性能:我们对带有三强度环状伪影的光子计数 CT 图像进行了大量实验。定性和定量结果表明,我们的网络模型在校正不同程度的环状伪影方面表现优异,同时与对比方法相比,我们的网络模型表现出更高的稳定性和鲁棒性:本文介绍了一种新型深度学习网络,旨在减轻光子计数 CT 图像中的环状伪影。结果表明,我们提出的网络模型是一种基于深度学习的抑制环状伪影的新方法,具有可行性和有效性。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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