CAREER: Ultrahigh-Resolution Magnetic Resonance Spectroscopic Imaging for Label-Free Molecular Imaging of the Brain

职业:用于大脑无标记分子成像的超高分辨率磁共振波谱成像

基本信息

  • 批准号:
    1944249
  • 负责人:
  • 金额:
    $ 51.73万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-02-01 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

Understanding how the brain works, including how to effectively treat brain disorders, is one of the most exciting scientific frontiers. Neuroimaging has significantly advanced this frontier by allowing noninvasive mapping of the brain’s anatomy and activity in unprecedented detail. However, new tools are needed to visualize molecular-level information in a living brain. Current methods are limited by several fundamental challenges, including the need of radioactive tracers or contrast agents, limited molecule recognition, noisy signals, long imaging time, and poor spatial resolution. The overall goal of this CAREER project is to develop a new generation of imaging technologies to address these challenges and enable label-free molecular imaging of the brain at unprecedented resolutions in space and time using magnetic resonance spectroscopic imaging (MRSI). Success of the research planned will significantly advance the field of molecular neuroimaging and enable new capabilities by simultaneously mapping many physiologically important molecules in the brain. These capabilities could revolutionize diagnosis and management of neurological diseases and mental disorders. The educational activities will integrate the research with curriculum innovation at the intersections of imaging science, machine learning and computational science and engineering. Activities include development of a Research Experiences for Undergraduates (REU) program to create unique training and professional development opportunities in biomedical imaging for underrepresented minorities in engineering and a new high-school student research internship program centered on neuroimaging.The investigator’s long-term research career goal is to develop a new generation of imaging technologies to enable label-free mapping of molecular profiles in the brain at unprecedented spatiotemporal resolutions and explore the potential of these technologies for studying brain functions and diseases at the molecular level. Towards this goal, building on the investigator’s expertise and previous contributions on fast MRI and MRSI, the goal of this CAREER project is to develop an innovative imaging framework to model, acquire and process MRSI data and enable new MRSI based molecular imaging capabilities. MRSI is a potentially powerful imaging modality that allows for simultaneous mapping of many physiologically important molecules in the human body without the need of radioactive tracers and contrast agents. However, to date, the development of MRSI still remains at its infancy because of its low signal-to-noise ratio (SNR), slow speed, poor spatial resolution, and susceptibility to system imperfection. The Research Plan is organized under five objectives: (1) Discovery of accurate and efficient low-dimensional models for high-dimensional MRSI signals by integrating biological priors, physics-based modeling and machine learning, to reduce the dimensionality of the imaging problem and enable better tradeoffs in speed, resolution and SNR; (2) Development of unconventional ultrafast data acquisition and data processing strategies that exploit the reduced dimensionality to achieve fast MRSI of the whole brain at the resolution level of functional MRI; (3) Development of novel mathematical formulations and efficient algorithms that work synergistically with the new models and acquisitions for optimal spatiospectral processing; (4) Integration of the new modeling, acquisition and processing methods to enable whole-brain mapping of metabolites and neurotransmitters and their biophysical properties, such as relaxation and diffusion parameters, for molecule-specific microstructural imaging of the brain and (5) Introduction of new dimensions into the proposed MRSI framework to map molecule dependent biophysical properties in 3D. These synergistic developments will transform MRSI from a slow, low-resolution modality to a powerful, high-resolution in vivo molecular neuroimaging tool and open up tremendous opportunities in studying brain biochemistry, microstructure and their connections to functions and disease processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
了解大脑如何工作,包括如何有效地治疗大脑疾病,是最令人兴奋的科学前沿之一。神经影像学通过以前所未有的细节对大脑的解剖和活动进行无创映射,大大推进了这一前沿。然而,需要新的工具来可视化活体大脑中的分子水平信息。目前的方法受到几个基本挑战的限制,包括需要放射性示踪剂或造影剂、有限的分子识别、噪声信号、长成像时间和差的空间分辨率。该CAREER项目的总体目标是开发新一代成像技术来应对这些挑战,并使用磁共振光谱成像(MRSI)以前所未有的空间和时间分辨率实现大脑的无标记分子成像。计划中的研究的成功将大大推进分子神经成像领域,并通过同时映射大脑中许多生理学上重要的分子来实现新的功能。这些能力可以彻底改变神经系统疾病和精神障碍的诊断和管理。教育活动将在成像科学,机器学习和计算科学与工程的交叉点将研究与课程创新相结合。活动包括本科生研究经验(REU)计划的发展,为工程领域代表性不足的少数民族创造独特的生物医学成像培训和专业发展机会,以及以神经成像为中心的新高中生研究实习计划。研究员的长期研究职业目标是开发新一代成像技术,以实现标签-以前所未有的时空分辨率自由绘制大脑中的分子图谱,并探索这些技术在分子水平上研究大脑功能和疾病的潜力。 为了实现这一目标,建立在研究者的专业知识和以前的贡献快速MRI和MRSI,这个CAREER项目的目标是开发一个创新的成像框架,建模,获取和处理MRSI数据,并使新的MRSI为基础的分子成像能力。MRSI是一种潜在的强大的成像模式,允许同时映射人体中的许多生理上重要的分子,而不需要放射性示踪剂和造影剂。然而,迄今为止,MRSI的发展仍然处于起步阶段,因为它的低信噪比(SNR),速度慢,空间分辨率差,系统的不完善性的敏感性。该研究计划分为五个目标:(1)通过整合生物先验、基于物理的建模和机器学习,为高维MRSI信号发现准确有效的低维模型,以降低成像问题的维度,并在速度、分辨率和SNR之间实现更好的权衡;(2)开发非传统的超快数据采集和数据处理策略,利用降维实现全脑的快速MRSI,达到功能MRI的分辨率水平;(3)开发新的数学公式和有效的算法,与新的模型和采集协同工作,以实现最佳的空间光谱处理;(4)整合新的建模、采集和处理方法,以实现代谢物和神经递质及其生物物理特性(例如弛豫和扩散参数)的全脑绘图,用于脑的分子特异性显微结构成像和(5)将新维度引入到所提出的MRSI框架中以在3D中映射分子依赖的生物物理特性。这些协同发展将MRSI从一个缓慢的,低分辨率的模式转变为一个强大的,高分辨率的体内分子神经成像工具,并在研究脑生物化学,微观结构及其与功能和疾病过程的联系方面开辟了巨大的机会。这个奖项反映了NSF的法定使命,并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量(18)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Rapid MRSI of the Brain on 7T Using Subspace-Based Processing
使用基于子空间的处理在 7T 上进行大脑的快速 MRSI
Fast volumetric diffusion-weighted MRSI: improved acquisition and data processing
快速体积扩散加权 MRSI:改进的采集和数据处理
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
High-SNR J-Resolved MRSI by jointly learning nonlinear representation and projection
通过联合学习非线性表示和投影实现高信噪比 J 分辨 MRSI
SNR-Enhancing Reconstruction for Multi-TE MRSI Using a Learned Nonlinear Low-Dimensional Model
使用学习非线性低维模型增强多 TE MRSI 的 SNR 重建
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Fan Lam其他文献

大小様々な銀河団の冷却コア内のガス揺らぎの系統解析
不同尺寸星系团冷却核心气体波动的系统分析
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    上田周太朗;梅津敬一;Ng;Fan Lam;一戸悠人, Molnar;Sandor M.;北山哲
  • 通讯作者:
    北山哲
Optical surveys of clusters of galaxies
星系团的光学巡天
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    上田周太朗;梅津敬一;Ng;Fan Lam;一戸悠人, Molnar;Sandor M.;北山哲;Masamune Oguri
  • 通讯作者:
    Masamune Oguri

Fan Lam的其他文献

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