三维低剂量、少视点心脏SPECT成像的可推广扩散框架。

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huidong Xie , Weijie Gan , Wei Ji , Xiongchao Chen , Alaa Alashi , Stephanie L. Thorn , Bo Zhou , Qiong Liu , Menghua Xia , Xueqi Guo , Yi-Hwa Liu , Hongyu An , Ulugbek S. Kamilov , Ge Wang , Albert J. Sinusas , Chi Liu
{"title":"三维低剂量、少视点心脏SPECT成像的可推广扩散框架。","authors":"Huidong Xie ,&nbsp;Weijie Gan ,&nbsp;Wei Ji ,&nbsp;Xiongchao Chen ,&nbsp;Alaa Alashi ,&nbsp;Stephanie L. Thorn ,&nbsp;Bo Zhou ,&nbsp;Qiong Liu ,&nbsp;Menghua Xia ,&nbsp;Xueqi Guo ,&nbsp;Yi-Hwa Liu ,&nbsp;Hongyu An ,&nbsp;Ulugbek S. Kamilov ,&nbsp;Ge Wang ,&nbsp;Albert J. Sinusas ,&nbsp;Chi Liu","doi":"10.1016/j.media.2025.103729","DOIUrl":null,"url":null,"abstract":"<div><div>Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting realistic applicability. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical <span><math><msup><mrow></mrow><mrow><mtext>99m</mtext></mrow></msup></math></span>Tc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic review from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103729"},"PeriodicalIF":11.8000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging\",\"authors\":\"Huidong Xie ,&nbsp;Weijie Gan ,&nbsp;Wei Ji ,&nbsp;Xiongchao Chen ,&nbsp;Alaa Alashi ,&nbsp;Stephanie L. Thorn ,&nbsp;Bo Zhou ,&nbsp;Qiong Liu ,&nbsp;Menghua Xia ,&nbsp;Xueqi Guo ,&nbsp;Yi-Hwa Liu ,&nbsp;Hongyu An ,&nbsp;Ulugbek S. Kamilov ,&nbsp;Ge Wang ,&nbsp;Albert J. Sinusas ,&nbsp;Chi Liu\",\"doi\":\"10.1016/j.media.2025.103729\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting realistic applicability. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical <span><math><msup><mrow></mrow><mrow><mtext>99m</mtext></mrow></msup></math></span>Tc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic review from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.</div></div>\",\"PeriodicalId\":18328,\"journal\":{\"name\":\"Medical image analysis\",\"volume\":\"107 \",\"pages\":\"Article 103729\"},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2025-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical image analysis\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1361841525002762\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image analysis","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361841525002762","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

SPECT心肌灌注成像被广泛用于冠状动脉疾病的诊断,但在低剂量和少视点采集环境下,图像质量会受到负面影响。虽然已经引入了各种深度学习方法来提高低剂量或少视点SPECT数据的图像质量,但以前的方法往往不能推广到不同的采集设置,限制了实际的适用性。这项工作引入了DiffSPECT-3D,这是一种用于心脏3D SPECT成像的扩散框架,可以有效地适应不同的采集设置,而无需进一步的网络重新训练或微调。利用图像和投影数据,提出了一种一致性策略,以确保每一步的扩散采样与低剂量/少视图投影测量值、图像数据和扫描仪几何形状一致,从而能够推广到不同的低剂量/少视图设置。结合CT的解剖空间信息和总变异约束,我们提出了一个2.5D条件策略,允许DiffSPECT-3D从整个图像体中观察3D上下文信息,解决了扩散模型中的3D记忆/计算问题。我们对来自795名患者的1325项临床99mTc四氟磷应激/休息研究进行了广泛的评估。将每项研究重构为5个不同的低计数水平和5个不同的投影少视图水平,分别从1%到50%和1到9视图进行模型评估。与心导管检查结果和核心脏病专家的诊断回顾相对照,本文的结果显示了在不影响临床表现的情况下实现低剂量和少视点SPECT成像的潜力。此外,DiffSPECT-3D可以直接应用于全剂量SPECT图像,以进一步提高图像质量,特别是在低剂量应力优先的心脏SPECT成像方案中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging

A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging

A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging
Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting realistic applicability. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic review from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
×
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:604180095
Book学术官方微信