Cross-Scanner Low-Dose Brain-PET Image Noise Reduction With Self-Ensembling

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jiale Wang;Rui Guo;Ying Miao;Song Xue;Yu Zhang;Kuangyu Shi;Guoyan Zheng;Biao Li
{"title":"Cross-Scanner Low-Dose Brain-PET Image Noise Reduction With Self-Ensembling","authors":"Jiale Wang;Rui Guo;Ying Miao;Song Xue;Yu Zhang;Kuangyu Shi;Guoyan Zheng;Biao Li","doi":"10.1109/TRPMS.2023.3347602","DOIUrl":null,"url":null,"abstract":"Deep learning models have shown great potential in reducing low-dose (LD) positron emission tomography (PET) image noise by estimating full-dose (FD) images from the corresponding LD images. Those models, however, when trained on paired LD-FD PET images from a source scanner, fail to generalize well when applied to LD PET images from a target scanner, due to a phenomenon called “domain drift.” In this study, we present a method for cross-scanner LD PET image noise reduction. This is done via a self-ensembling framework using a limited number of paired LD-FD PET images and a large number of LD PET images from the target scanner. The self-ensembling framework leverages the paired 2-D slices from both scanners to learn a regression model. It additionally incorporates a consistency loss on the LD PET images from the target scanner to enhance the model’s generalization capability. We conduct experiments on three datasets, respectively, acquired from three different scanners, including a GE Discovery MI (DMI) scanner, a Siemens Biograph Vision 450 (Vision) scanner, and a UI uMI 780 (uMI) scanner. Results from our comprehensive experiments demonstrate the generalization capability of our method.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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/10375271/","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

Deep learning models have shown great potential in reducing low-dose (LD) positron emission tomography (PET) image noise by estimating full-dose (FD) images from the corresponding LD images. Those models, however, when trained on paired LD-FD PET images from a source scanner, fail to generalize well when applied to LD PET images from a target scanner, due to a phenomenon called “domain drift.” In this study, we present a method for cross-scanner LD PET image noise reduction. This is done via a self-ensembling framework using a limited number of paired LD-FD PET images and a large number of LD PET images from the target scanner. The self-ensembling framework leverages the paired 2-D slices from both scanners to learn a regression model. It additionally incorporates a consistency loss on the LD PET images from the target scanner to enhance the model’s generalization capability. We conduct experiments on three datasets, respectively, acquired from three different scanners, including a GE Discovery MI (DMI) scanner, a Siemens Biograph Vision 450 (Vision) scanner, and a UI uMI 780 (uMI) scanner. Results from our comprehensive experiments demonstrate the generalization capability of our method.
利用自组装技术降低跨扫描仪低剂量脑 PET 图像噪声
深度学习模型通过从相应的低剂量(LD)图像中估计全剂量(FD)图像,在减少低剂量(LD)正电子发射断层扫描(PET)图像噪声方面显示出巨大的潜力。然而,当这些模型在来自源扫描仪的成对 LD-FD PET 图像上进行训练时,由于一种称为 "域漂移 "的现象,当应用到来自目标扫描仪的 LD PET 图像时,这些模型不能很好地泛化。在这项研究中,我们提出了一种跨扫描仪 LD PET 图像降噪方法。该方法通过一个自组装框架来实现,该框架使用数量有限的配对 LD-FD PET 图像和大量来自目标扫描仪的 LD PET 图像。自组装框架利用两台扫描仪的配对二维切片来学习回归模型。此外,它还在目标扫描仪的 LD PET 图像上加入了一致性损失,以增强模型的泛化能力。我们在三个数据集上进行了实验,这三个数据集分别来自三个不同的扫描仪,包括 GE Discovery MI(DMI)扫描仪、Siemens Biograph Vision 450(Vision)扫描仪和 UI uMI 780(uMI)扫描仪。综合实验结果证明了我们方法的通用能力。
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 求助全文
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信