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Advancing MRI & MRS Technologies for Studying Human Brain Function and Energetics

Advancing MRI & MRS Technologies for Studying Human Brain Function and Energetics
推进核磁共振成像
批准号:
8827010
负责人:
Wei Chen
金额:
$46.87万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-26 至 2017-06-30

项目摘要

项目成果

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中文摘要
翻译
 描述(申请人提供):磁共振成像(MRI)和活体磁共振波谱(MRS)技术已成为成像大脑结构、功能、连通性、神经化学和神经能量学以及研究神经疾病不可或缺的工具。然而,即使使用最先进的技术,要获得高的MRI/MRS检测灵敏度、空间和时间成像分辨率,足以解决基本的和具有挑战性的神经科学问题,仍然是一个挑战。用于改善MRI/MRS性能的流行范例主要包括增加磁场强度,由于许多技术和安全考虑(即,高比吸收率(SAR)),这可能已经达到人体研究的实际可达到的极限,以及增加接收器通道计数,这也最终由于尺寸减小的线圈的噪声特性而受到限制。为了缓解这些主要限制,R24建议依靠两个机构的跨学科研究努力和领先专家的专业知识,开创了一种全新的创新工程解决方案,该解决方案使用超高介电常数(UHDC)材料与超高场MRI/MRS技术相结合,协同提高信噪比并同时降低射频功率需求,并在空间/时间分辨率方面实现比当前最先进的磁共振技术前所未有的改进。我们将使用7特斯拉(T)和10.5T全身扫描仪开发和优化用于人脑研究的uHDC材料的原型。此外,我们将开发和评估创新的uHDC-MR技术用于尖端神经科学研究的新用途和能力。其中一项先导性研究是使用7T超高空间分辨率1H MRI,结合来自扩散加权图像的解剖连接,在人类视觉皮质的柱状层和皮质层水平上绘制神经回路和静息状态连接的功能图。二是将uHDC技术与新近发展起来的活体31P和17OMRS技术相结合,无创、可靠地成像静息和激活状态下人脑的氧耗和ATP代谢率、脑血流量、氧提取分数和烟酰胺腺嘌呤二核苷酸(NAD)氧化还原状态。这项拟议的研究将把目前的神经成像开发范式转变为一种高效、低成本的工程解决方案,该方案将从超高分辨率和超高场中获得倍增的收益,并导致下一代MRI/MRS技术和仪器的诞生。这种进步将加速人脑成像和神经科学研究,超越现有技术所能达到的水平,推动新的研究方向,并改变我们对人脑功能和功能障碍的理解。
英文摘要
 DESCRIPTION (provided by applicant): Magnetic resonance (MR) imaging (MRI) and in vivo MR spectroscopy (MRS) techniques have become indispensable tools for imaging brain structure, function, connectivity, neurochemistry and neuroenergetics, and for investigating neurological disorders. However, it remains a challenge to achieve superior MRI/MRS detection sensitivity, spatial and temporal imaging resolutions adequate for addressing fundamental and challenging neuroscience questions even with the most advanced technology. The prevailing paradigms for improving MRI/MRS performance largely invoke increasing the magnetic field strength, which may have reached practically achievable limits for human studies due to many technological and safety (i.e., high specific absorption rate (SAR)) concerns, and increasing the receiver channel count which is also ultimately limited due to noise characteristics of coils of decreasing size. To alleviate these major limitations, this R24 proposal relies on the interdisciplinary research efforts and expertise of leading experts across two institutions to pioneer an entirely innovative engineering solution that uses the ultra-high dielectric constant (uHDC) material incorporated with ultrahigh-field MRI/MRS techniques for synergistically increase signal-to-noise ratio and concurrently reduce RF power demand, and for achieving unprecedented improvements in spatial/temporal resolution over the current state-of-the-art MR technologies. We will develop and optimize prototypes of uHDC material for human brain studies using 7 Tesla (T) and 10.5T whole-body human scanners. Moreover, we will exploit and assess the new utility and capability of the innovative uHDC-MR technology for cutting-edge neuroscience research. One pilot study is the functional mapping of neural circuits and resting-state connectivity at the level of columns and cortical layers in the human visual cortex with ultrahigh spatial resolution 1H MRI at 7T, complemented with anatomical connectivity derived from diffusion weighted images for tractography. The other one is to combine the uHDC technique with newly developed in vivo 31P and 17O MRS techniques for noninvasively and reliably imaging the cerebral metabolic rates of oxygen consumption and ATP, cerebral blood flow, oxygen extraction fraction and nicotinamide adenine dinucleotide (NAD) redox state in the human brain at resting and activated states. The proposed research will shift the current paradigm of neuroimaging development towards an efficient, cost-effective engineering solution that will attain multiplicative gains from uHDC and ultrahigh fields, and lead to next generation o MRI/MRS technology and instrument. Such advancement will accelerate human brain imaging and neuroscience research beyond what can be achieved through existing technology, promote new research directions, and transform our understanding regarding the human brain function and dysfunction.
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会议论文
An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
  • 批准号:
    10564824
  • 项目类别:
  • 资助金额:
    $54.37万
  • 财政年份:
    2023
  • 负责人:
    Wei Chen
  • 依托单位:
Establishing translational neuroimaging tools for quantitative assessment of energy metabolism and metabolic reprogramming in healthy and diseased human brain at 7T
  • 批准号:
    10714863
  • 项目类别:
  • 资助金额:
    $63.02万
  • 财政年份:
    2023
  • 负责人:
    Wei Chen
  • 依托单位:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
海外基金