Highly Accelerated Distortion-Free Diffusion-WeightedMR Imaging at Ultra High Field (7T): Gray Matter Characterization
超高场 (7T) 下的高加速无失真扩散加权磁共振成像:灰质表征
基本信息
- 批准号:215187497
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2012
- 资助国家:德国
- 起止时间:2011-12-31 至 2018-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Single-shot echo-planar imaging (EPI) at ultra-high field (UHF) enables fast brain imaging with high spatial resolution. It is very beneficial for functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) studies to measure brain function and to characterize information about brain tissue microstructure and used as a standard method. However, a well-known problem in EPI is strong susceptibility-induced geometric distortion, especially at UHF such as 7T. In DWI, in addition, distortions in EPI vary due to eddy-currents depending on the direction of diffusion encoding gradients and become a major challenge. During the first funding period of this project (2012-2015) we have successfully implemented an online distortion correction package for fMRI and extended the method to DWI. Due to limited spatial resolution of EPI, strong distortions, and signal loss appear in cortical regions. Therefore, DWI has been applied almost exclusively to the investigation of structure and pathology in white matter (WM) up to now. As an alternative to standard EPI, we developed a novel distortion-free technique that allows unprecedented high spatial resolution and fidelity of in vivo human brain DWI. With our new method, gray matter microstructure becomes accessible non-invasively although measurement times are prolonged. In the continuation of this project, our goal covers i) full development of a comprehensive (i.e. susceptibility- and eddy-current-induced) distortion correction package and its implementation for standard DWI, ii) further research on our new technique for very high-resolution DWI to reduce scan time and improve the image fidelity by suppressing flow- and motion-induced artifacts, and iii) detailed in vivo characterization of gray matter diffusion using very high-resolution diffusion images. This will allow comprehensive characterization of gray matter diffusion in the entire human brain in vivo for the first time and serve as a reference for future scientific and clinical applications.
超高场(UHF)下的单次激发回波平面成像(EPI)能够实现具有高空间分辨率的快速脑成像。它对于功能磁共振成像(fMRI)和扩散加权成像(DWI)研究测量脑功能和表征有关脑组织微观结构的信息非常有益,并用作标准方法。然而,EPI中的一个众所周知的问题是强磁化率引起的几何失真,特别是在UHF例如7 T处。此外,在DWI中,EPI中的失真由于取决于扩散编码梯度的方向的涡流而变化,并且成为主要挑战。在该项目的第一个资助期(2012-2015),我们成功地实现了一个在线失真校正包的功能磁共振成像和扩展的方法DWI。由于EPI有限的空间分辨率,在皮层区域出现强烈的失真和信号丢失。因此,扩散加权成像一直几乎完全应用于白色物质(WM)的结构和病理的调查到现在。作为标准EPI的替代方案,我们开发了一种新的无失真技术,可以实现前所未有的高空间分辨率和保真度的活体人脑DWI。使用我们的新方法,灰质微结构变得无创访问,虽然测量时间延长。在这个项目的继续,我们的目标包括i)全面发展一个全面的(即磁化率和涡流诱导的)失真校正包及其在标准DWI中的实现,ii)进一步研究我们用于极高分辨率DWI的新技术,以通过抑制流动和运动诱导的伪影来减少扫描时间并提高图像保真度,和iii)使用非常高分辨率的扩散图像的灰质扩散的详细体内表征。这将首次在体内对整个人脑中的灰质扩散进行全面表征,并为未来的科学和临床应用提供参考。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
High-resolution distortion-free diffusion imaging using hybrid spin-warp and echo-planar PSF-encoding approach
- DOI:10.1016/j.neuroimage.2017.01.008
- 发表时间:2017-03-01
- 期刊:
- 影响因子:5.7
- 作者:In, Myung-Ho;Posnansky, Oleg;Speck, Oliver
- 通讯作者:Speck, Oliver
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Professor Dr. Oliver Speck其他文献
Professor Dr. Oliver Speck的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Professor Dr. Oliver Speck', 18)}}的其他基金
Adaptive distortion correction techniques for high-field magnetic resonance neuroimaging
高场磁共振神经成像的自适应畸变校正技术
- 批准号:
5444777 - 财政年份:2005
- 资助金额:
-- - 项目类别:
Research Grants
Hocheffiziente 31P spektroskopische Bildgebung mittels SSFP
使用 SSFP 进行高效 31P 光谱成像
- 批准号:
5430558 - 财政年份:2004
- 资助金额:
-- - 项目类别:
Research Grants
Ultraschnelle kernspintomographische Parameterbildgebung mittels Single-Shot-Multi-Echo Verfahren
使用单次多回波方法的超快磁共振成像参数成像
- 批准号:
5235532 - 财政年份:2000
- 资助金额:
-- - 项目类别:
Independent Junior Research Groups
相似海外基金
Digital Solutions For Accelerated Battery Testing
加速电池测试的数字解决方案
- 批准号:
10107050 - 财政年份:2024
- 资助金额:
-- - 项目类别:
EU-Funded
Micro-manufacturing of tissue patterned organ-chips for accelerated deployment of new medicines (Patterned OrganChips)
用于加速新药部署的组织图案化器官芯片的微制造(图案化器官芯片)
- 批准号:
EP/Z531261/1 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Research Grant
Accelerated carbon dioxide release from sedimentary rocks in a warming world
在变暖的世界中沉积岩加速二氧化碳释放
- 批准号:
NE/Y000838/1 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Research Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
Accelerated discovery of ultra-fast ionic conductors with machine learning
通过机器学习加速超快离子导体的发现
- 批准号:
24K08582 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Grant-in-Aid for Scientific Research (C)
CAREER: Open-source GPU-accelerated computational infrastructure for coastal fluid-structure interaction in extreme hydrodynamic conditions
职业:极端水动力条件下沿海流固耦合的开源 GPU 加速计算基础设施
- 批准号:
2338313 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
Artificial intelligence coupled to automation for accelerated medicine design
人工智能与自动化相结合,加速药物设计
- 批准号:
EP/Z533038/1 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Research Grant
Development of Understanding and Preparation for Machine learning for Accelerated Carbonation Technology Pelletisation Process - Carbon8
加速碳酸化技术造粒过程的机器学习的理解和准备的发展 - Carbon8
- 批准号:
10091589 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Collaborative R&D
Human models for accelerated robot learning and human-robot interaction
用于加速机器人学习和人机交互的人体模型
- 批准号:
DP240101458 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Discovery Projects
Drag Prediction over Rough Surfaces using Hardware-Accelerated Simulations
使用硬件加速模拟对粗糙表面进行阻力预测
- 批准号:
DE230100754 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Discovery Early Career Researcher Award














{{item.name}}会员




