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

工件(错误) 特征(语言学) 计算机科学 戒指(化学) 人工智能 模式识别(心理学) 化学 语言学 哲学 有机化学
作者
Wei Cui,Haipeng Lv,Jiping Wang,Yanyan Zheng,Zhongyi Wu,Hui Zhao,Jian Zheng,Ming Li
出处
期刊:Journal of X-ray Science and Technology [IOS Press]
卷期号:32 (3): 529-547
标识
DOI:10.3233/xst-230396
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
秋暝寒衣完成签到,获得积分10
3秒前
完美世界应助bfs采纳,获得10
4秒前
橘子sungua完成签到,获得积分10
5秒前
111完成签到,获得积分10
5秒前
7秒前
9秒前
10秒前
可乐加冰完成签到,获得积分10
10秒前
David驳回了Ant应助
10秒前
11秒前
12秒前
量子星尘发布了新的文献求助10
13秒前
思源应助果粒多采纳,获得10
13秒前
14秒前
好滴捏发布了新的文献求助10
15秒前
bfs发布了新的文献求助10
16秒前
WN发布了新的文献求助10
17秒前
17秒前
慕青应助小白采纳,获得10
18秒前
AAACharlie发布了新的文献求助10
18秒前
热情的达发布了新的文献求助10
18秒前
orixero应助lucky李采纳,获得10
19秒前
19秒前
momo发布了新的文献求助10
20秒前
20秒前
21秒前
guo完成签到,获得积分10
22秒前
可期完成签到,获得积分10
23秒前
24秒前
wsj发布了新的文献求助10
24秒前
果粒多发布了新的文献求助10
25秒前
科目三应助ylq采纳,获得30
26秒前
liupc2019发布了新的文献求助20
27秒前
张雯思发布了新的文献求助10
30秒前
希望天下0贩的0应助momo采纳,获得10
30秒前
31秒前
32秒前
梦华完成签到 ,获得积分10
33秒前
34秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989297
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253893
捐赠科研通 3270097
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809158