扩散张量成像分析管道的网格化

M. Caan, F. Vos, A. V. Kampen, S. Olabarriaga, L. Vliet
{"title":"扩散张量成像分析管道的网格化","authors":"M. Caan, F. Vos, A. V. Kampen, S. Olabarriaga, L. Vliet","doi":"10.1109/CCGRID.2010.99","DOIUrl":null,"url":null,"abstract":"Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific ‘atlas’ where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Gridifying a Diffusion Tensor Imaging Analysis Pipeline\",\"authors\":\"M. Caan, F. Vos, A. V. Kampen, S. Olabarriaga, L. Vliet\",\"doi\":\"10.1109/CCGRID.2010.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific ‘atlas’ where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.\",\"PeriodicalId\":444485,\"journal\":{\"name\":\"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCGRID.2010.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGRID.2010.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

弥散张量MRI (DTI)是一种较新的图像采集方式,可以帮助识别大脑神经束的疾病过程。由于此类数据的庞大和复杂性质,其分析需要在网格环境中更有效地执行的新的和复杂的管道。我们介绍了过去四年在开发和移植DTI分析管道到电网方面的进展。从从命令行提交的简单作业开始,我们转向了基于工作流的实现,最后进入了最终用户可以通过web浏览器访问的web服务。分析算法从基本的发展到最先进的技术,目前能够自动计算特定人群的“图谱”,即使是复杂的大脑区域也能以解剖学上正确的方式描述。性能统计数据显示多年来有了明显的改善,这代表了技术推动和应用程序拉动的共同利益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gridifying a Diffusion Tensor Imaging Analysis Pipeline
Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific ‘atlas’ where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.
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
自引率
0.00%
发文量
0
×
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