Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy

多线圈多核附加系统,用于杜氏肌营养不良症临床场强 NMR 生物标志物检测

基本信息

项目摘要

SUMMARY Nuclear Magnetic Resonance (NMR) offers established advantages for biomarker detection, but has not become clinically standard largely due to a lack of sensitivity in the detection of non-1H nuclei at clinical field strengths. Array coils offer a natural and practical approach to increase sensitivity in MR experiments, and are standard in nearly all imaging applications. As such, clinical scanners are typically equipped with multiple receiver channels for 1H (imaging), but only a single broadband receiver channel capable of receiving second-nuclei information. Collection of non-1H data is therefore limited to serial studies per nucleus (long experiment times and changing of coils between studies) and cannot achieve increased sensitivity through the use of array coils. This project proposes a practical way to address clinical hardware limitations and validates the utility of the approach with application to Duchenne Muscular Dystrophy (DMD). DMD is a genetic disorder that affects ~1:5,000 newborn boys and causes degeneration of skeletal and cardiac muscle, resulting in a loss of ambulation in the teenage years, cardiac/respiratory issues in the late 20’s, and death in the third decade. The value of animal models in the study of DMD (and all diseases) is well established, but is of particular importance to DMD at this time due to the exploding development of experimental therapies – molecular, cellular, and pharmacologic. One of the most established colonies of golden retrievers with muscular dystrophy (GRMD) in the world resides at Texas A&M University – an animal model proven to be superior for relevant assessment of therapies. This project will show the viability of achieving the benefits of clinical NMR-based biomarker detection with a straightforward hardware addition and simultaneously allow for data collection in a manner that is painless, non-invasive, and minimizes anesthesia time for dogs in DMD studies at the Texas Institute for Preclinical Sciences (TIPS). Aim 1- Design and construct multi-tuned RF coil arrays for detection of 1H, 31P, and 23Na. Completion of this aim will result in RF coils designed for SNR-enhanced in vivo NMR-based biomarker detection with minimal changes to the current GRMD scanning protocol. Aim 2- Build an add-on multi-channel frequency translation system to provide multi-channel multinuclear spectroscopy on the Siemens 3T scanner. Completion of this aim will result in a modular system, straightforwardly interfaced to the existing 32-channel 1H receiver that will allow acquisition of up to 16 channels of 1H data and up to eight channel data from two additional nuclei – in this case, 31P and 23Na. Aim 3- Obtain 1H-31P-23Na data from already-characterized existing ex vivo preserved muscle samples. Completion of this aim will result in the initial testing of the frequency translation system and will verify the sensitivity and specificity of NMR-based detection. Aim 4- Obtain in vivo 1H-31P-23Na data from GRMD disease models as an “add on” procedure to current scans using the hardware interface to modify the 3T scanner for multi-nuclear multi-channel acquisition. Completion of this aim will confirm the utility of in vivo NMR-based biomarker detection as a practical option in clinical scanning.
总结 核磁共振(NMR)为生物标志物检测提供了既定的优势,但还没有成为 这主要是由于在临床场强下检测非1H核缺乏灵敏度。 阵列线圈提供了一种自然和实用的方法来提高MR实验的灵敏度,并且是标准的 几乎所有的成像应用。因此,临床扫描仪通常配备有多个接收器通道 用于1H(成像),但是只有能够接收第二核信息的单个宽带接收器通道。 因此,非1H数据的收集仅限于每个核的系列研究(长实验时间和变化 研究之间的线圈数),并且不能通过使用阵列线圈来实现增加的灵敏度。这个项目 提出了一种解决临床硬件限制的实用方法,并验证了该方法的实用性, Duchenne Muscular Dystrophy(DMD)DMD是一种遗传性疾病,影响约1:5,000的新生儿 导致骨骼和心肌的退化,导致青少年的骨骼肌萎缩, 20年代后期出现心脏/呼吸问题,第三个十年死亡。动物模型的价值 DMD(以及所有疾病)的研究已经建立,但目前对DMD特别重要, 到实验疗法的爆炸式发展-分子,细胞和药理学。之一 世界上大多数患有肌营养不良症(GRMD)的金毛猎犬定居在德克萨斯州 A&M大学-动物模型被证明是上级的相关评估的治疗。该项目将 显示了实现基于临床NMR的生物标志物检测的益处的可行性, 硬件增加,同时允许以无痛、非侵入性的方式收集数据, 在德克萨斯临床前科学研究所(TIPS)的DMD研究中,最大限度地缩短了犬的麻醉时间。目的 1-设计和构建用于检测1H、31 P和23 Na的多调谐RF线圈阵列。完成本 目标将导致设计用于SNR增强的基于NMR的体内生物标志物检测的RF线圈, 对当前GRMD扫描协议的更改。目标2-建立附加多通道频率 翻译系统提供多通道多核光谱西门子3 T扫描仪。 完成这一目标将产生一个模块化系统,直接连接到现有的32通道1H 接收器,将允许采集多达16个通道的1H数据和多达8个通道的数据,从两个额外的 核-在这种情况下,31 P和23 Na。目标3-从已表征的现有实验室获得1H-31 P-23 Na数据 活体保存的肌肉样本。这一目标的完成将导致频率的初步测试 翻译系统,并将验证基于NMR的检测的灵敏度和特异性。目的4-在体内获得 来自GRMD疾病模型的1H-31 P-23 Na数据,作为当前扫描的“附加”程序, 硬件接口,以修改3 T扫描仪,用于多核多通道采集。完成 该目的将证实基于NMR的体内生物标记检测作为临床扫描中的实际选择的效用。

项目成果

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Mary Preston McDougall其他文献

Mary Preston McDougall的其他文献

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{{ truncateString('Mary Preston McDougall', 18)}}的其他基金

Research supplement to promote diversity: Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy
促进多样性的研究补充:用于杜氏肌营养不良症临床场强 NMR 生物标志物检测的多线圈多核附加系统
  • 批准号:
    10442104
  • 财政年份:
    2021
  • 资助金额:
    $ 31.91万
  • 项目类别:
Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy
多线圈多核附加系统,用于杜氏肌营养不良症临床场强 NMR 生物标志物检测
  • 批准号:
    10553651
  • 财政年份:
    2020
  • 资助金额:
    $ 31.91万
  • 项目类别:
Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy
多线圈多核附加系统,用于杜氏肌营养不良症临床场强 NMR 生物标志物检测
  • 批准号:
    10552836
  • 财政年份:
    2020
  • 资助金额:
    $ 31.91万
  • 项目类别:
Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy
多线圈多核附加系统,用于杜氏肌营养不良症临床场强 NMR 生物标志物检测
  • 批准号:
    10745780
  • 财政年份:
    2020
  • 资助金额:
    $ 31.91万
  • 项目类别:
Multi-coil multi-nuclear add-on system for clinical field strength NMR-based biomarker detection for Duchenne Muscular Dystrophy
多线圈多核附加系统,用于杜氏肌营养不良症临床场强 NMR 生物标志物检测
  • 批准号:
    9974125
  • 财政年份:
    2020
  • 资助金额:
    $ 31.91万
  • 项目类别:
Sixteen channel carbon-13 phased array spectroscopy at 7 Tesla
7 特斯拉十六通道碳 13 相控阵光谱
  • 批准号:
    8584134
  • 财政年份:
    2013
  • 资助金额:
    $ 31.91万
  • 项目类别:
Sixteen channel carbon-13 phased array spectroscopy at 7 Tesla
7 特斯拉十六通道碳 13 相控阵光谱
  • 批准号:
    8686839
  • 财政年份:
    2013
  • 资助金额:
    $ 31.91万
  • 项目类别:

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