Bidirectional Condition Diffusion Probabilistic Models for PET Image Denoising

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-01-17 DOI:10.1109/TRPMS.2024.3355247
Chenyu Shen;Changjun Tie;Ziyuan Yang;Na Zhang;Yi Zhang
{"title":"Bidirectional Condition Diffusion Probabilistic Models for PET Image Denoising","authors":"Chenyu Shen;Changjun Tie;Ziyuan Yang;Na Zhang;Yi Zhang","doi":"10.1109/TRPMS.2024.3355247","DOIUrl":null,"url":null,"abstract":"Low-count positron emission tomography (PET) imaging is an effective way to reduce the radiation risk of PET at the cost of a low-signal-to-noise ratio. Our study aims to denoise low-count PET images in an unsupervised mode since the mainstream methods rely on paired data, which is not always feasible in clinical practice. We adopt the diffusion probabilistic model in consideration of its strong generation ability. Our model consists of two stages. In the training stage, we learn a score function network via evidence lower-bound (ELBO) optimization. In the sampling stage, the trained score function and low-count image are employed to generate the corresponding high-count image under two handcrafted conditions. One is based on restoration in latent space, and the other is based on noise insertion in latent space. Thus, our model is named the bidirectional condition diffusion probabilistic model (BC-DPM). The Zubal phantom and real patient whole-body data are utilized to evaluate our model. The experiments show that our model achieves better performance in both qualitative and quantitative respects compared to several traditional and recently proposed learning-based methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10401984","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10401984/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Low-count positron emission tomography (PET) imaging is an effective way to reduce the radiation risk of PET at the cost of a low-signal-to-noise ratio. Our study aims to denoise low-count PET images in an unsupervised mode since the mainstream methods rely on paired data, which is not always feasible in clinical practice. We adopt the diffusion probabilistic model in consideration of its strong generation ability. Our model consists of two stages. In the training stage, we learn a score function network via evidence lower-bound (ELBO) optimization. In the sampling stage, the trained score function and low-count image are employed to generate the corresponding high-count image under two handcrafted conditions. One is based on restoration in latent space, and the other is based on noise insertion in latent space. Thus, our model is named the bidirectional condition diffusion probabilistic model (BC-DPM). The Zubal phantom and real patient whole-body data are utilized to evaluate our model. The experiments show that our model achieves better performance in both qualitative and quantitative respects compared to several traditional and recently proposed learning-based methods.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于 PET 图像去噪的双向条件扩散概率模型
低计数正电子发射断层扫描(PET)成像是降低 PET 辐射风险的有效方法,但代价是低信噪比。我们的研究旨在以无监督模式对低计数 PET 图像进行去噪,因为主流方法依赖于配对数据,而配对数据在临床实践中并不总是可行的。考虑到扩散概率模型的强大生成能力,我们采用了扩散概率模型。我们的模型由两个阶段组成。在训练阶段,我们通过证据下限(ELBO)优化学习分数函数网络。在采样阶段,利用训练好的分数函数和低计数图像,在两种手工条件下生成相应的高计数图像。一种是基于潜空间的还原,另一种是基于潜空间的噪声插入。因此,我们的模型被命名为双向条件扩散概率模型(BC-DPM)。我们利用 Zubal 模型和真实病人的全身数据来评估我们的模型。实验表明,与几种传统的和最近提出的基于学习的方法相比,我们的模型在定性和定量方面都取得了更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
期刊最新文献
Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents Introducing IEEE Collabratec IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information Member Get-a-Member (MGM) Program
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1