Short Axis EPI for Diffusion Tensor MRI at High Field

用于高场弥散张量 MRI 的短轴 EPI

基本信息

  • 批准号:
    7507451
  • 负责人:
  • 金额:
    $ 61.63万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2008
  • 资助国家:
    美国
  • 起止时间:
    2008-09-15 至 2012-06-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): MOTIVATION - Diffusion Tensor Imaging (DTI) is an MRI method for noninvasive quantitative mapping of anisotropic water diffusion, thereby allowing the investigation of white matter (WM) microstructure. DTI holds tremendous potential for aiding the understanding of pathophysiologies of white matter and delayed maturation. In addition, it enables non-invasive tracing of WM pathways in tumor patients and is also helpful for diagnosing patients harboring non-focal disease. However, DTI still suffers from technical shortcomings which become even more problematic with increasing magnetic fields. This is unfortunate since increased magnetic field strengths offer substantially more SNR that is desperately needed for an SNR-starved method such as DTI. Because of their size and other specific requirements, especially pediatric patients would be major benefactors from DTI high field imaging - particularly when combined with more powerful fMRI and structural imaging achievable at these higher fields. Obstacles to using DTI at higher fields include: FOV requirements, lack of cooperation, increased motion, increase off-resonance artifacts, significant RF inhomogeneity, and increased RF energy deposition. AIMS - The main objective of the proposed project is to create significant improvements in DTI at high field (i.e. 3T and 7T) via novel acquisition/reconstruction techniques that reduce distortions, improve immunity to motion, diminish RF deposition and flip angle variation, and provide better spatial resolution so that improved pediatric and adult high-field DTI is enabled. The specific aims are to develop and optimize acquisition and reconstruction methods for diffusion tensor short-axis-readout EPI (sr-EPI) (A.1), and investigate and help to rekindle interest in 7T DTI by incorporating optimized sr-EPI DTI sampling strategies with parallel transmit as well as to further boost scan efficiency by adding multi-echo readouts (A.2). Anticipating the move toward higher-field strengths, these aims are designed to provide a robust imaging protocol for the clinical environment (3T), while providing the means to overcome short-comings in current ultra-high field strength methodologies. METHODS - In A.1, novel schemes for off-resonance, eddy-current, and motion correction will be developed. In addition, an efficient parallel imaging reconstruction algorithm will be developed to compliment a family of proposed SNR-, SAR-, and scan time-efficient sr-EPI techniques. In A.2, work with an experienced group of collaborators will allow the implementation of parallel transmit technology on our 7T. Together with an 'exact' distortion model, these B1/B0 correction methodologies ideally compliment a fast variant of sr-EPI. By adding RF-refocused multi-blade/blind readout the scan efficiency will be increased even further. All proposed acquisition and reconstruction techniques will be optimized both in simulations and phantom studies. A total number of subjects of 200 (children and adults) will be enrolled over this five year period for extensive testing. Optimal image quality will be determined by quantitative metrics and human observers. SIGNIFICANCE -We believe successful attainment of these aims promises to significant improvements in DTI, reaching beyond high field and pediatric patients, and hence greater overall utility of DTI. Abnormalities in WM and tract projections could provide crucial insights in the pathophysiology of several diseases that attack white matter, and further the understanding of specific neurodevelopmental trajectories of children with and without WM disorders. The success of our research effort would be of great value since it would build the basic methodological framework at high field for further clinically focused studies and basic neuroscience research.
描述(由申请人提供):动机-扩散张量成像(DTI)是一种非侵入性定量测绘各向异性水扩散的MRI方法,从而允许研究白质(WM)微观结构。DTI在帮助理解白质和延迟成熟的病理生理方面具有巨大的潜力。此外,它可以无创地追踪肿瘤患者的WM通路,也有助于诊断患有非局灶性疾病的患者。然而,DTI仍然存在技术缺陷,随着磁场的增加,这些缺陷变得更加成问题。这是不幸的,因为增加的磁场强度提供了更多的信噪比,这对于像DTI这样的信噪比匮乏的方法来说是迫切需要的。由于它们的尺寸和其他特殊要求,特别是儿科患者将是DTI高场成像的主要受益者-特别是当与更强大的fMRI和结构成像相结合时,这些高场成像可以实现。在更高的领域使用DTI的障碍包括:视场要求、缺乏合作、运动增加、非共振伪影增加、显著的射频不均匀性和增加的射频能量沉积。目标-拟议项目的主要目标是通过新颖的采集/重建技术来显著改善高视场(即3T和7T)的DTI,减少扭曲,提高对运动的免疫,减少RF沉积和翻转角度变化,并提供更好的空间分辨率,从而改善儿童和成人的高视场DTI。具体目标是开发和优化扩散张量短轴读出EPI (sr-EPI) (A.1)的采集和重建方法,并通过将优化的sr-EPI DTI采样策略与并行传输相结合,以及通过增加多回波读出进一步提高扫描效率(A.2)来研究并帮助重新激发对7T DTI的兴趣。这些目标旨在为临床环境(3T)提供强大的成像方案,同时为克服当前超高场强方法的缺点提供手段。方法:在A.1中,将开发非共振、涡流和运动校正的新方案。此外,将开发一种高效的并行成像重建算法,以补充一系列提出的SNR-、SAR-和扫描时效率高的sr-EPI技术。在A.2中,与经验丰富的合作者合作将允许在我们的7T上实现并行传输技术。与“精确”失真模型一起,这些B1/B0校正方法理想地补充了sr-EPI的快速变体。通过增加射频重聚焦多刀片/盲读出,扫描效率将进一步提高。所有提出的采集和重建技术将在模拟和模拟研究中进行优化。总共有200名受试者(儿童和成人)将在这五年的时间里被招募进行广泛的测试。最佳图像质量将由定量指标和人类观察者来确定。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(2)

数据更新时间:{{ journalArticles.updateTime }}

{{ 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 }}

ROLAND BAMMER其他文献

ROLAND BAMMER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('ROLAND BAMMER', 18)}}的其他基金

Cardiac Diffusion Imaging for Heart Transplant Surveillance
用于心脏移植监测的心脏弥散成像
  • 批准号:
    8650639
  • 财政年份:
    2014
  • 资助金额:
    $ 61.63万
  • 项目类别:
A Prototype Wireless Digital MR Spectrometer on a Single Integrated Circuit
单个集成电路上的无线数字磁共振波谱仪原型
  • 批准号:
    8710219
  • 财政年份:
    2013
  • 资助金额:
    $ 61.63万
  • 项目类别:
A Prototype Wireless Digital MR Spectrometer on a Single Integrated Circuit
单个集成电路上的原型无线数字磁共振波谱仪
  • 批准号:
    8597817
  • 财政年份:
    2013
  • 资助金额:
    $ 61.63万
  • 项目类别:
Real-Time MRI Motion Correction System
实时 MRI 运动校正系统
  • 批准号:
    7987431
  • 财政年份:
    2010
  • 资助金额:
    $ 61.63万
  • 项目类别:
DIFFUSION IMAGING ?MR NEUROIMAGING
弥散成像?MR 神经成像
  • 批准号:
    8169831
  • 财政年份:
    2010
  • 资助金额:
    $ 61.63万
  • 项目类别:
Real-Time MRI Motion Correction System
实时 MRI 运动校正系统
  • 批准号:
    8141396
  • 财政年份:
    2010
  • 资助金额:
    $ 61.63万
  • 项目类别:
Real-Time MRI Motion Correction System
实时 MRI 运动校正系统
  • 批准号:
    8323818
  • 财政年份:
    2010
  • 资助金额:
    $ 61.63万
  • 项目类别:
Novel Acquisition Methods for Diffusion MRI
扩散 MRI 的新采集方法
  • 批准号:
    7379478
  • 财政年份:
    2009
  • 资助金额:
    $ 61.63万
  • 项目类别:
Novel Acquisition Methods for Diffusion MRI
扩散 MRI 的新采集方法
  • 批准号:
    7837741
  • 财政年份:
    2009
  • 资助金额:
    $ 61.63万
  • 项目类别:
DIFFUSION IMAGING ?MR NEUROIMAGING
弥散成像?MR 神经成像
  • 批准号:
    7955357
  • 财政年份:
    2009
  • 资助金额:
    $ 61.63万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Standard 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:
    $ 61.63万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 61.63万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了