Novel RF Coils for In Vivo MR Applications at High Field

适用于高场体内 MR 应用的新型射频线圈

基本信息

  • 批准号:
    6888131
  • 负责人:
  • 金额:
    $ 48.86万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2003
  • 资助国家:
    美国
  • 起止时间:
    2003-07-01 至 2008-04-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Technical developments in high-field magnetic resonance imaging (MRI) and spectroscopy (MRS) have been accelerated because of the advantage of high sensitivity that significantly improves the capability and reliability for human applications. This advancement is further stimulated by the unique image contrasts available at high fields for fMRI and clinical diagnosis. However, to fully realize the advancement, many challenges must be resolved. Major problems associated with high fields are the difficulty of radiofrequency (RF) coil designs and the complex magnetic fields (B1) of RF coil when the wavelength of RF wave approaches RF coil's size. They result in (i) severe degradation of coil quality factor and NMR sensitivity, (ii) limitation for designing large size coils with high operating frequency and (iii) complex B1 field distributions and difficulty for quantifying MRI intensity. These complications necessitate innovative strategies to overcome the problems associated with RF coils at high fields. In collaboration with Dr. Yang from Penn State University, a comprehensive project is proposed in this grant application for addressing the RF engineering challenges at high fields. The major goals will focus on (i) developing a host of robust and efficient high-field RF coils for human and animal studies using an innovative design based on the microstrip transmission line (MTL) approach, (ii) studying the B1 field behavior in human head at high fields using computer simulation based on RF field modeling and MRI measurements, and studying the implications of B1 field on MRI quantification, (iii) conducting a series of study for systematically evaluating the proposed coils in comparison with other existing coils. Successful outcomes from this research will provide an alternative and satisfactory solution of RF coil design at high fields and result in significant technological advances in high-field RF coil engineering for in vivo MR applications.
描述(由申请人提供): 高场磁共振成像(MRI)和波谱(MRS)的技术发展已经加速,因为高灵敏度的优势,显着提高了人类应用的能力和可靠性。这一进步进一步刺激了独特的图像对比度,可在高场功能磁共振成像和临床诊断。然而,要充分实现这一进步,必须解决许多挑战。与高场相关联的主要问题是射频(RF)线圈设计的困难和当RF波的波长接近RF线圈的尺寸时RF线圈的复杂磁场(B1)。它们导致(i)线圈品质因数和NMR灵敏度的严重退化,(ii)设计具有高工作频率的大尺寸线圈的限制,以及(iii)复杂的B1场分布和量化MRI强度的困难。这些并发症需要创新的策略来克服与高场RF线圈相关的问题。在与来自宾夕法尼亚州立大学的杨博士的合作中,在此资助申请中提出了一个综合项目,以解决高场RF工程的挑战。主要目标将集中在(i)使用基于微带传输线(MTL)方法的创新设计开发用于人类和动物研究的大量鲁棒且高效的高场RF线圈,(ii)使用基于RF场建模和MRI测量的计算机模拟研究高场下人类头部中的B1场行为,并研究B1场对MRI量化的影响,(iii)进行一系列研究,有系统地评估建议的线圈,并与其他现有线圈作比较。本研究的成功结果将为高场RF线圈设计提供一种替代和令人满意的解决方案,并导致体内MR应用的高场RF线圈工程的重大技术进步。

项目成果

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Wei Chen其他文献

Wei Chen的其他文献

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{{ truncateString('Wei Chen', 18)}}的其他基金

An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
用于结直肠癌肿瘤芽检测和风险分层的集成深度学习模型。
  • 批准号:
    10564824
  • 财政年份:
    2023
  • 资助金额:
    $ 48.86万
  • 项目类别:
Establishing translational neuroimaging tools for quantitative assessment of energy metabolism and metabolic reprogramming in healthy and diseased human brain at 7T
建立转化神经影像工具,用于定量评估 7T 健康和患病人脑的能量代谢和代谢重编程
  • 批准号:
    10714863
  • 财政年份:
    2023
  • 资助金额:
    $ 48.86万
  • 项目类别:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH:新的先进机器学习框架,用于挖掘异构眼部数据以加速
  • 批准号:
    10601180
  • 财政年份:
    2022
  • 资助金额:
    $ 48.86万
  • 项目类别:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH:新的先进机器学习框架,用于挖掘异构眼部数据以加速
  • 批准号:
    10665804
  • 财政年份:
    2022
  • 资助金额:
    $ 48.86万
  • 项目类别:
Cellular Interactions in Vascular Calcification of Chronic Kidney Disease
慢性肾病血管钙化中的细胞相互作用
  • 批准号:
    10525401
  • 财政年份:
    2022
  • 资助金额:
    $ 48.86万
  • 项目类别:
Console Replacement and Upgrade of 9.4 Tesla Animal Instrument
9.4特斯拉动物仪控制台更换升级
  • 批准号:
    10414184
  • 财政年份:
    2022
  • 资助金额:
    $ 48.86万
  • 项目类别:
Deep-learning-based prediction of AMD and its progression with GWAS and fundus image data
基于 GWAS 和眼底图像数据的 AMD 及其进展的深度学习预测
  • 批准号:
    10226322
  • 财政年份:
    2020
  • 资助金额:
    $ 48.86万
  • 项目类别:
Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field
推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性
  • 批准号:
    10043972
  • 财政年份:
    2020
  • 资助金额:
    $ 48.86万
  • 项目类别:
Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field
推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性
  • 批准号:
    10268184
  • 财政年份:
    2020
  • 资助金额:
    $ 48.86万
  • 项目类别:
Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field
推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性
  • 批准号:
    10463737
  • 财政年份:
    2020
  • 资助金额:
    $ 48.86万
  • 项目类别:
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