Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury.

Emily L Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C Giza, Robert F Asarnow, Paul M Thompson
{"title":"Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury.","authors":"Emily L Dennis,&nbsp;Faisal Rashid,&nbsp;Julio Villalon-Reina,&nbsp;Gautam Prasad,&nbsp;Joshua Faskowitz,&nbsp;Talin Babikian,&nbsp;Richard Mink,&nbsp;Christopher Babbitt,&nbsp;Jeffrey Johnson,&nbsp;Christopher C Giza,&nbsp;Robert F Asarnow,&nbsp;Paul M Thompson","doi":"10.1007/978-3-319-55524-9_4","DOIUrl":null,"url":null,"abstract":"<p><p>Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T<sub>1</sub>-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.</p>","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-55524-9_4","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-319-55524-9_4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T1-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.

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
多模式登记提高儿童创伤性脑损伤的群体歧视。
创伤性脑损伤(TBI)可以破坏大脑白质(WM)的完整性,导致可能持续数年的功能和认知障碍。患者组内存在相当大的异质性,这使组分析变得复杂。在这里,我们提出了改进的通道识别工作流程,自动多图谱通道提取(自动化),评估改进注册的效果。与标准工作流程相比,使用特定于研究的模板提高了分组分类的准确性。加入多模态配准,包括来自扩散加权成像(DWI)、t1加权成像和流体衰减反演恢复(FLAIR)的信息,进一步提高了分类精度。我们还研究了特定束是否比其他束对群体分类贡献更大。胼胝体的部分贡献最大,双侧束之间存在意想不到的不对称。
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
自引率
0.00%
发文量
0
期刊最新文献
Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II
×
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