Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.

IF 11.7 1区 医学 Q1 CELL BIOLOGY
Cell Reports Medicine Pub Date : 2025-07-15 Epub Date: 2025-06-27 DOI:10.1016/j.xcrm.2025.102207
Jongjun Won, Grace Yoojin Lee, Sungyang Jo, Jihyun Lee, Sangjin Lee, Jae Seung Kim, Changhwan Sung, Jungsu S Oh, Kyum-Yil Kwon, Soo Bin Park, Joonsang Lee, Jieun Yum, Sun Ju Chung, Namkug Kim
{"title":"Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.","authors":"Jongjun Won, Grace Yoojin Lee, Sungyang Jo, Jihyun Lee, Sangjin Lee, Jae Seung Kim, Changhwan Sung, Jungsu S Oh, Kyum-Yil Kwon, Soo Bin Park, Joonsang Lee, Jieun Yum, Sun Ju Chung, Namkug Kim","doi":"10.1016/j.xcrm.2025.102207","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate diagnosis and precise estimation of disease progression states are crucial for developing effective treatment plans for patients with parkinsonism. Although various deep learning-based computer-aided diagnostic models have demonstrated benefits, they have been relatively underexplored in parkinsonism owing to limited data and lack of external validation. We introduce the hierarchical wavelet diffusion autoencoder (HWDAE), a generative self-supervised model trained with 1,934 dopamine transporter positron emission tomography (DAT PET) images. HWDAE learns relevant disease traits during generative training, prior to supervision with human labels, as evidenced by its ability to synthesize realistic images representing different disease states of Parkinson's disease. The pretrained HWDAE is subsequently adapted for two differential diagnostic tasks and one disease progression estimation task, tested on images from two medical centers. Our training approach introduces a paradigm for deep learning research utilizing PET and expands the potential of DAT PET as a biomarker for Parkinson's disease.</p>","PeriodicalId":9822,"journal":{"name":"Cell Reports Medicine","volume":" ","pages":"102207"},"PeriodicalIF":11.7000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.xcrm.2025.102207","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate diagnosis and precise estimation of disease progression states are crucial for developing effective treatment plans for patients with parkinsonism. Although various deep learning-based computer-aided diagnostic models have demonstrated benefits, they have been relatively underexplored in parkinsonism owing to limited data and lack of external validation. We introduce the hierarchical wavelet diffusion autoencoder (HWDAE), a generative self-supervised model trained with 1,934 dopamine transporter positron emission tomography (DAT PET) images. HWDAE learns relevant disease traits during generative training, prior to supervision with human labels, as evidenced by its ability to synthesize realistic images representing different disease states of Parkinson's disease. The pretrained HWDAE is subsequently adapted for two differential diagnostic tasks and one disease progression estimation task, tested on images from two medical centers. Our training approach introduces a paradigm for deep learning research utilizing PET and expands the potential of DAT PET as a biomarker for Parkinson's disease.

通过扩散模型的自我监督学习增强3D多巴胺转运体成像作为帕金森病的生物标志物。
准确诊断和准确估计疾病进展状态对于制定有效的帕金森患者治疗计划至关重要。尽管各种基于深度学习的计算机辅助诊断模型已经显示出益处,但由于数据有限和缺乏外部验证,它们在帕金森病中的探索相对不足。我们引入了分层小波扩散自编码器(HWDAE),这是一种生成式自监督模型,由1934张多巴胺传输体正电子发射断层扫描(DAT PET)图像训练而成。在人类标签监督之前,HWDAE在生成训练过程中学习相关的疾病特征,其能够合成代表帕金森病不同疾病状态的逼真图像证明了这一点。预训练的HWDAE随后适用于两个鉴别诊断任务和一个疾病进展估计任务,对来自两个医疗中心的图像进行测试。我们的训练方法引入了一种利用PET进行深度学习研究的范例,并扩大了DAT PET作为帕金森病生物标志物的潜力。
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
Cell Reports Medicine
Cell Reports Medicine Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
15.00
自引率
1.40%
发文量
231
审稿时长
40 days
期刊介绍: Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine. Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.
×
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