Radial Echo Volumar Imaging
径向回波容积成像
基本信息
- 批准号:9980730
- 负责人:
- 金额:$ 32.51万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-15 至 2024-03-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAccelerationAlgorithmsBenignBrainBrain imagingBreathingComputer softwareDetectionDevelopmentDiffusionDimensionsEcho-Planar ImagingFrequenciesFunctional Magnetic Resonance ImagingHealthcareImageMagnetic Resonance ImagingMapsMethodologyMethodsModelingModernizationModificationMorphologic artifactsMotionNatureNoisePeriodicityPhasePhysiologic pulsePhysiologicalPredispositionPropertyProtonsRadialResearch PersonnelResearch Project GrantsResearch Project SummariesResearch ProposalsResolutionRestRotationRunningSamplingScanningSchemeSignal TransductionSliceSpeedStructureTechniquesTechnologyTimeTranslationsbaseblood oxygen level dependentdensityflexibilityimage reconstructionimaging approachimaging modalityimprovedinnovationinterestmotion sensitivitynovelperfusion imagingreconstructionresiliencesuccesstemporal measurement
项目摘要
Project Summary
This research project “Radial Echo Volumar Imaging” proposes the development of MRI acquisition and
reconstruction methods based on a novel versatile non-Cartesian sampling concept for fast motion-corrected
imaging. The technique expands upon Echo Planar Imaging (EPI), which is the most widely utilized fast MRI
acquisition and is the standard method for various applications ranging from functional MRI to diffusion and
perfusion imaging. Recently EPI has also been shown to be promising for rapid structural imaging including
simultaneous multi-parametric MRI. Most modern EPI approaches are based on volumetric imaging methods
as they permit high isotropic spatial resolution, improved Signal to Noise Ratio per unit time, and parallel
imaging acceleration along the third dimension. A challenge of volumetric imaging however is the requirement
for segmentation due to gradient and physiological limitations that leads to increased motion sensitivity and
other physiological effects.
Radial sampling offers several advantages with regards to segmented acquisitions including robustness to
motion due to intrinsic self-navigation from oversampling the center of k-space. Radial sampling also has the
benefit of producing benign “streaking” aliasing artifacts compared to Cartesian allowing for large accelerations
and an efficient use of parallel imaging methods. A further advantage of radial sampling is that the in-plane
dimension is sampled quickly by frequency encoding leading to higher in-plane resolution and less distortion
with low time penalty. In this project we propose to utilize these advantages to develop innovative methodology
for rapid and robust brain imaging that should also prove to be important for many other imaging applications
including body MRI.
The Scientific Premise of this proposal is that an optimal rapid MRI acquisition can be obtained by using three-
dimensional radial EPI trajectories and generalized model-based reconstructions. We propose an innovative
Radial Echo Volumar Imaging (REVI) acquisition created by adding gradient encoding along the third direction
of a radial EPI acquisition to create SMS and 3D rotated “Stack-of-Stars” sampling for high parallel imaging
acceleration while allowing for optimal tradeoffs in temporal and spatial resolutions. The self-navigation
properties of the radial trajectories will provide motion robustness and continuous golden-angle rotation will
permit variable temporal resolutions and reordering of the acquisition. The multi-echo nature of REVI will also
allow for simultaneous multi-parametric structural scanning. The proposed technology requires no special
hardware and can be run on any scanner by any investigator.
项目概要
该研究项目“径向回波体积成像”提出了 MRI 采集和成像技术的发展
基于新颖的通用非笛卡尔采样概念的快速运动校正重建方法
成像。该技术扩展了回波平面成像 (EPI),这是使用最广泛的快速 MRI
采集,是从功能性 MRI 到扩散和扫描等各种应用的标准方法
灌注成像。最近,EPI 也被证明在快速结构成像方面具有广阔的前景,包括
同时多参数 MRI。大多数现代 EPI 方法都基于体积成像方法
因为它们允许高各向同性空间分辨率、提高每单位时间的信噪比以及并行
沿三维的成像加速度。然而,体积成像的一个挑战是要求
用于由于梯度和生理限制导致运动敏感性增加的分割
其他生理效应。
径向采样在分段采集方面具有多种优势,包括稳健性
由于对 k 空间中心进行过采样而产生的固有自导航而产生的运动。径向采样还具有
与笛卡尔相比,产生良性“条纹”混叠伪像的好处是允许大加速度
以及并行成像方法的有效使用。径向采样的另一个优点是面内采样
通过频率编码快速采样尺寸,从而实现更高的面内分辨率和更少的失真
时间惩罚低。在这个项目中,我们建议利用这些优势来开发创新方法
用于快速、稳健的脑成像,这对于许多其他成像应用也很重要
包括身体核磁共振成像。
该提案的科学前提是,可以通过使用三个来获得最佳的快速 MRI 采集:
维度径向 EPI 轨迹和基于广义模型的重建。我们提出了一个创新的
通过沿第三方向添加梯度编码创建径向回波容积成像 (REVI) 采集
径向 EPI 采集以创建 SMS 和 3D 旋转“星栈”采样以实现高并行成像
加速,同时允许时间和空间分辨率的最佳权衡。自我导航
径向轨迹的特性将提供运动鲁棒性,并且连续的黄金角旋转将
允许可变的时间分辨率和采集的重新排序。 REVI 的多回波性质也将
允许同时进行多参数结构扫描。所提出的技术不需要特殊的
硬件,可以由任何调查员在任何扫描仪上运行。
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(0)
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 }}
Victor Andrew Stenger其他文献
Victor Andrew Stenger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('Victor Andrew Stenger', 18)}}的其他基金
Fast Whole-Brain Direct Myelin Magnetic Resonance Imaging
快速全脑直接髓磷脂磁共振成像
- 批准号:
9261522 - 财政年份:2016
- 资助金额:
$ 32.51万 - 项目类别:
Spectral Spatial RF Pulses for Gradient Echo fMRI
用于梯度回波 fMRI 的频谱空间射频脉冲
- 批准号:
8239585 - 财政年份:2010
- 资助金额:
$ 32.51万 - 项目类别:
Spectral Spatial RF Pulses for Gradient Echo fMRI
用于梯度回波 fMRI 的频谱空间射频脉冲
- 批准号:
8437270 - 财政年份:2010
- 资助金额:
$ 32.51万 - 项目类别:
Spectral Spatial RF Pulses for Gradient Echo fMRI
用于梯度回波 fMRI 的频谱空间射频脉冲
- 批准号:
8055365 - 财政年份:2010
- 资助金额:
$ 32.51万 - 项目类别:
Spectral Spatial RF Pulses for Gradient Echo fMRI
用于梯度回波 fMRI 的频谱空间射频脉冲
- 批准号:
7861946 - 财政年份:2010
- 资助金额:
$ 32.51万 - 项目类别:
相似海外基金
Shared and Distributed Memory Parallel Pre-Conditioning and Acceleration Algorithms for "Spline- Enhanced" Spatial Discretisations
用于“样条增强”空间离散化的共享和分布式内存并行预处理和加速算法
- 批准号:
2907459 - 财政年份:2023
- 资助金额:
$ 32.51万 - 项目类别:
Studentship
Efficient algorithms and succinct data structures for acceleration of telescoping and related problems
用于加速伸缩及相关问题的高效算法和简洁数据结构
- 批准号:
RGPIN-2021-03147 - 财政年份:2022
- 资助金额:
$ 32.51万 - 项目类别:
Discovery Grants Program - Individual
Acceleration framework for training deep learning by cooperative with algorithms and computer architectures
通过与算法和计算机架构合作训练深度学习的加速框架
- 批准号:
21K17768 - 财政年份:2021
- 资助金额:
$ 32.51万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Efficient algorithms and succinct data structures for acceleration of telescoping and related problems
用于加速伸缩及相关问题的高效算法和简洁数据结构
- 批准号:
RGPIN-2021-03147 - 财政年份:2021
- 资助金额:
$ 32.51万 - 项目类别:
Discovery Grants Program - Individual
Material and Device Building Blocks for Hardware Acceleration of Machine Learning and Artificial Intelligence Algorithms
用于机器学习和人工智能算法硬件加速的材料和设备构建模块
- 批准号:
2004791 - 财政年份:2020
- 资助金额:
$ 32.51万 - 项目类别:
Continuing Grant
CIF: Small: Collaborative Research: Acceleration Algorithms for Large-scale Nonconvex Optimization
CIF:小型:协作研究:大规模非凸优化的加速算法
- 批准号:
1909291 - 财政年份:2019
- 资助金额:
$ 32.51万 - 项目类别:
Standard Grant
Acceleration of trigger algorithms with FPGAs at the LHC implemented using higher-level programming languages
使用高级编程语言在 LHC 上使用 FPGA 加速触发算法
- 批准号:
ST/S005560/1 - 财政年份:2019
- 资助金额:
$ 32.51万 - 项目类别:
Training Grant
CIF: Small: Collaborative Research: Acceleration Algorithms for Large-scale Nonconvex Optimization
CIF:小型:协作研究:大规模非凸优化的加速算法
- 批准号:
1909298 - 财政年份:2019
- 资助金额:
$ 32.51万 - 项目类别:
Standard Grant
Acceleration of trigger algorithms with FPGAs at the LHC implemented using higher-level programming languages
使用高级编程语言在 LHC 上使用 FPGA 加速触发算法
- 批准号:
2348748 - 财政年份:2019
- 资助金额:
$ 32.51万 - 项目类别:
Studentship
OAC Core: Small: Enabling High-fidelity Turbulent Reacting-Flow Simulations through Advanced Algorithms, Code Acceleration, and High-order Methods for Extreme-scale Computing
OAC 核心:小型:通过高级算法、代码加速和超大规模计算的高阶方法实现高保真湍流反应流模拟
- 批准号:
1909379 - 财政年份:2019
- 资助金额:
$ 32.51万 - 项目类别:
Standard Grant














{{item.name}}会员




