ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI.

ArXiv Pub Date : 2024-11-21
Mengze Gao, Zachary Shah, Xiaozhi Cao, Nan Wang, Daniel Abraham, Kawin Setsompop
{"title":"ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI.","authors":"Mengze Gao, Zachary Shah, Xiaozhi Cao, Nan Wang, Daniel Abraham, Kawin Setsompop","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601784/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"","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

Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations.

分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ACE-Net:应用于高 b 值螺旋扩散磁共振成像的场不完善估计自动增强卷积网络。
B0-不均匀性和扩散编码引起的涡流所产生的时空磁场变化会对螺旋、EPI 和三维锥体等快速图像编码方案造成损害,从而导致不良的图像伪影。在这项工作中,通过将自动对焦指标与深度学习相结合,并利用预期场缺陷的紧凑基础表示法,开发了一种数据驱动的自动估计这些场缺陷的方法。该方法被应用于高 b 值的单次螺旋扩散核磁共振成像,获得了 B0 和涡流的精确估计,从而无需额外的外部校准即可进行高质量的图像重建。
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
Metastability in networks of nonlinear stochastic integrate-and-fire neurons. On the linear scaling of entropy vs. energy in human brain activity, the Hagedorn temperature and the Zipf law. Timing consistency of T cell receptor activation in a stochastic model combining kinetic segregation and proofreading. Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle. Adversarial Attacks on Large Language Models in Medicine.
×
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