An integrated vascular MR imaging suite in brain diseases

脑部疾病的综合血管 MR 成像套件

基本信息

  • 批准号:
    10330590
  • 负责人:
  • 金额:
    $ 56.22万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-04-01 至 2024-12-31
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract: Cerebrovascular imaging has a broad impact in a variety of brain disorders, including cerebrovascular diseases such as stroke, arterial stenosis, Moymoya disease, small vessel diseases, and vascular dementia, but also in other neurological conditions such as brain tumor and traumatic brain injury. Current clinical practice of cerebrovascular imaging requires multiple scans and, in some cases, multiple visits in order to obtain a complete assessment of the brain’s vascular health that includes perfusion, hemodynamic parameter, and flow reserve. This limitation increases patient burden and significantly escalates the cost of care. Therefore, the goal of the present project is to develop novel methods to perform an integrated vascular (iVas) imaging that provides all relevant physiological information in a single scan (<10 minutes). The proposed iVas-MRI technique will apply concomitant O2 and CO2 gas inhalation (but with different timing) and will simultaneously measure cerebral blood volume (CBV), cerebrovascular reactivity (CVR), bolus time-to-peak (TTP), and functional connectivity networks from the same dataset. Aim 1 will develop three key components of the iVas-MRI technique, specifically concomitant O2 and CO2 gas-inhalation paradigm, high spatial-resolution MRI pulse sequence, and multi-parametric data processing algorithm. A cloud-based computation platform will also be developed for standardization of the analysis and future dissemination of the technique. Aim 2 will conduct validation and multi-vendor assessment of the iVas-MRI technique. We will compare results of the iVas-MRI technique to those of standard techniques and will examine across-vendor reproducibility of the proposed technique by scanning each participant on three MRI systems manufactured by General Electric, Philips, and Siemens, respectively. Aim 3 will apply the technique in patients with Moyamoya disease and study its potential value in both the diagnosis and treatment monitoring of this condition. We will first examine the utility of iVas-MRI in predicting clinical outcomes in a cross-sectional setting. Then, through serial MRIs, we will examine the utility of iVas-MRI in differentiating treatment benefits of two most commonly performed surgical procedures in Moyamoya patients, specifically direct versus indirect bypass surgery. The long-term impact of this work on clinical practice is that patients with cerebrovascular diseases will have their vascular imaging scan done in just one visit of less than 10 minutes (as opposed to multiple visits and several scans). Additionally, patients who are allergic to conventional contrast agent will have access to an alternative contrast agent (i.e. O2 and CO2 gases) for their vascular imaging needs.
项目摘要/摘要: 脑血管成像对包括脑血管在内的各种脑部疾病有广泛的影响。 中风、动脉狭窄、烟雾病、小血管疾病和血管性痴呆等疾病, 但在其他神经疾病中也是如此,如脑瘤和创伤性脑损伤。当前的临床实践 脑血管成像需要多次扫描,在某些情况下,还需要多次就诊才能获得 全面评估脑血管健康状况,包括脑血流灌注、血流动力学参数和血流量 保留。这一限制增加了患者的负担,并显著增加了护理成本。因此, 本项目的目标是开发新的方法来执行集成血管(IVA)成像, 在一次扫描中提供所有相关的生理信息(&lt;10分钟)。 建议的IVAS-MRI技术将同时应用O2和CO2气体吸入(但不同 定时),并将同时测量脑血容量(CBV)、脑血管反应性(CVR)、团注 峰值时间(TTP),以及同一数据集中的功能连接网络。目标1将开发三个关键 IVAS-MRI技术的组成部分,特别是伴随的O2和CO2气体吸入范例,高 空间分辨率MRI脉冲序列,多参数数据处理算法。基于云的 还将开发计算平台,以标准化分析和未来传播 技术。AIM 2将对IVAS-MRI技术进行验证和多供应商评估。我们会 将IVAS-MRI技术与标准技术的结果进行比较,并将跨供应商进行检查 通过在由以下公司制造的三个MRI系统上扫描每个参与者来验证所建议技术的重复性 通用电气、飞利浦和西门子分别是。目标3将该技术应用于烟雾病患者 并研究其在该病的诊断和治疗监测中的潜在价值。我们会 首先,在横断面研究中,检查IVAS-MRI在预测临床结果中的作用。然后,通过 系列磁共振成像,我们将检查静脉注射磁共振成像在区分两种最常见的 对烟雾病患者进行手术,特别是直接搭桥手术和间接搭桥手术。 这项工作对临床实践的长期影响是,脑血管疾病患者将 在不到10分钟的一次就诊时间内完成血管成像扫描(而不是多次就诊 和几次扫描)。此外,对传统造影剂过敏的患者将有机会获得 替代造影剂(即O2和CO2气体),以满足其血管成像需求。

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 }}

Hanzhang Lu其他文献

Hanzhang Lu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('Hanzhang Lu', 18)}}的其他基金

ISMRM Workshop on Perfusion MRI: From Head to Toe
ISMRM 灌注 MRI 研讨会:从头到脚
  • 批准号:
    10391735
  • 财政年份:
    2022
  • 资助金额:
    $ 56.22万
  • 项目类别:
TRD1: Quantitative Imaging of Physiological Markers
TRD1:生理标志物的定量成像
  • 批准号:
    10614608
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers
生理、代谢和解剖生物标志物的 MRI 资源
  • 批准号:
    10614604
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers
生理、代谢和解剖生物标志物的 MRI 资源
  • 批准号:
    10439901
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
TRD1: Quantitative Imaging of Physiological Markers
TRD1:生理标志物的定量成像
  • 批准号:
    10439903
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
TRD1: Quantitative Imaging of Physiological Markers
TRD1:生理标志物的定量成像
  • 批准号:
    10270098
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers
生理、代谢和解剖生物标志物的 MRI 资源
  • 批准号:
    10270096
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
Blood-brain barrier dysfunction in Alzheimer's disease: from humans to animal models
阿尔茨海默病的血脑屏障功能障碍:从人类到动物模型
  • 批准号:
    10178195
  • 财政年份:
    2021
  • 资助金额:
    $ 56.22万
  • 项目类别:
Non-contrast MR imaging of blood-brain-barrier permeability in Alzheimer's disease
阿尔茨海默病血脑屏障通透性的非对比磁共振成像
  • 批准号:
    10621142
  • 财政年份:
    2020
  • 资助金额:
    $ 56.22万
  • 项目类别:
Non-contrast MR imaging of blood-brain-barrier permeability in Alzheimer's disease
阿尔茨海默病血脑屏障通透性的非对比磁共振成像
  • 批准号:
    10390475
  • 财政年份:
    2020
  • 资助金额:
    $ 56.22万
  • 项目类别:

相似海外基金

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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 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:
    $ 56.22万
  • 项目类别:
    Continuing Grant
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:
    $ 56.22万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了