Imaging the structural connectome with hybrid MRI-microscopy tractography

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2025-02-06 DOI:10.1016/j.media.2025.103498
Silei Zhu , Istvan N. Huszar , Michiel Cottaar , Greg Daubney , Nicole Eichert , Taylor Hanayik , Alexandre A. Khrapitchev , Rogier B. Mars , Jeroen Mollink , Jerome Sallet , Connor Scott , Adele Smart , Saad Jbabdi , Karla L. Miller , Amy F.D. Howard
{"title":"Imaging the structural connectome with hybrid MRI-microscopy tractography","authors":"Silei Zhu ,&nbsp;Istvan N. Huszar ,&nbsp;Michiel Cottaar ,&nbsp;Greg Daubney ,&nbsp;Nicole Eichert ,&nbsp;Taylor Hanayik ,&nbsp;Alexandre A. Khrapitchev ,&nbsp;Rogier B. Mars ,&nbsp;Jeroen Mollink ,&nbsp;Jerome Sallet ,&nbsp;Connor Scott ,&nbsp;Adele Smart ,&nbsp;Saad Jbabdi ,&nbsp;Karla L. Miller ,&nbsp;Amy F.D. Howard","doi":"10.1016/j.media.2025.103498","DOIUrl":null,"url":null,"abstract":"<div><div>Mapping how neurons are structurally wired into whole-brain networks can be challenging, particularly in larger brains where 3D microscopy is not available. Multi-modal datasets combining MRI and microscopy provide a solution, where high resolution but 2D microscopy can be complemented by whole-brain but lowresolution MRI. However, there lacks unified approaches to integrate and jointly analyse these multi-modal data in an insightful way. To address this gap, we introduce a data-fusion method for hybrid MRI-microscopy fibre orientation and connectome reconstruction. Specifically, we complement precise “in-plane” orientations from microscopy with “through-plane” information from MRI to construct 3D hybrid fibre orientations at resolutions far exceeding that of MRI whilst preserving microscopy's myelin specificity, resulting in superior fibre tracking. Our method is openly available, can be deployed on standard 2D microscopy, including different microscopy contrasts, and is species agnostic, facilitating neuroanatomical investigation in both animal models and human brains.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103498"},"PeriodicalIF":11.8000,"publicationDate":"2025-02-06","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/S1361841525000465","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

Abstract

Mapping how neurons are structurally wired into whole-brain networks can be challenging, particularly in larger brains where 3D microscopy is not available. Multi-modal datasets combining MRI and microscopy provide a solution, where high resolution but 2D microscopy can be complemented by whole-brain but lowresolution MRI. However, there lacks unified approaches to integrate and jointly analyse these multi-modal data in an insightful way. To address this gap, we introduce a data-fusion method for hybrid MRI-microscopy fibre orientation and connectome reconstruction. Specifically, we complement precise “in-plane” orientations from microscopy with “through-plane” information from MRI to construct 3D hybrid fibre orientations at resolutions far exceeding that of MRI whilst preserving microscopy's myelin specificity, resulting in superior fibre tracking. Our method is openly available, can be deployed on standard 2D microscopy, including different microscopy contrasts, and is species agnostic, facilitating neuroanatomical investigation in both animal models and human brains.

Abstract Image

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
用混合核磁共振成像-显微牵引成像技术成像结构连接体
绘制神经元如何在结构上连接到全脑网络可能具有挑战性,特别是在无法使用3D显微镜的较大大脑中。结合MRI和显微镜的多模态数据集提供了一种解决方案,其中高分辨率但2D显微镜可以由全脑但低分辨率MRI补充。然而,目前还缺乏统一的方法来整合和联合分析这些多模态数据。为了解决这一差距,我们引入了一种用于混合mri显微镜纤维定向和连接体重建的数据融合方法。具体来说,我们将显微镜精确的“平面内”方向与MRI的“平面内”信息相补充,以远远超过MRI的分辨率构建3D混合纤维方向,同时保留显微镜的髓磷脂特异性,从而实现优越的纤维跟踪。我们的方法是公开可用的,可以部署在标准的二维显微镜上,包括不同的显微镜对比,并且是物种不可知论的,便于在动物模型和人类大脑中进行神经解剖学研究。
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
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.
期刊最新文献
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition Depth-Induced Prompt Learning for Laparoscopic Liver Landmark Detection Robust non-rigid image-to-patient registration for contactless dynamic thoracic tumor localization using recursive deformable diffusion models C2HFusion: Clinical context-driven hierarchical fusion of multimodal data for personalized and quantitative prognostic assessment in pancreatic cancer
×
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:604180095
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