A New J-Resolved MRSI Framework for Whole-Brain Simultaneous Metabolite and Neurotransmitter Mapping

用于全脑同步代谢物和神经递质图谱的新 J-Resolved MRSI 框架

• 批准号: 10057847
• 负责人: Fan Lam
• 金额: $ 56.55万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10057847
• 关键词: Address; Algorithmic Software; Basic Science; Biochemical; Biological Markers; Brain; Brain Mapping; Brain imaging; Clinical; Computer Simulation; Contrast Media; Coupling; Data; Diagnosis; Dimensions; Disease; Equation; Evaluation; Evolution; Experimental Designs; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Image; Imaging Techniques; Imaging problem; Imaging technology; Learning; Machine Learning; Maps; Mechanics; Metabolic; Methods; Modality; Modeling; Molecular; Monitor; Nerve Degeneration; Neurodegenerative Disorders; Neurologic; Neurotransmitters; Noise; Organ; Pathologic; Patients; Physics; Physiologic pulse; Physiological; Physiological Processes; Process; Reproducibility; Research; Resolution; Sampling; Scanning; Scheme; Scientist; Sclerosis; Signal Transduction; Software Tools; Solid; Spectrum Analysis; Speed; Technology; Temporal Lobe Epilepsy; Time; Tissues; Training; Validation; Variant; Water; base; clinical application; clinical translation; computerized data processing; deep neural network; design; experimental study; healthy volunteer; high dimensionality; imaging study; improved; in vivo; innovation; interest; magnetic field; magnetic resonance spectroscopic imaging; molecular imaging; nervous system disorder; neuroimaging; novel; novel strategies; patient population; potential biomarker; quantum; reconstruction; relating to nervous system; simulation; spectroscopic imaging; success; tool

PROJECT SUMMARY/ABSTRACT The metabolite and neurotransmitter profiles of neural tissues provide a unique window into brain’s physiological state and can be used to extract potential biomarkers for detecting and characterizing neurodegenerative diseases. Magnetic resonance spectroscopic imaging (MRSI) allows simultaneous mapping and quantification of a number of metabolites and neurotransmitters without exogenous contrast agents thus promised tremendous opportunities for molecular imaging of the brain. However, due to several fundamental technical challenges, including low SNR, poor spatial resolution, long imaging time and inaccurate separation of spectrally overlapping molecular signals, most in vivo MRSI studies to date are still limited to very low-resolution experiments (~1cm3 voxel size) with small brain coverages. The primary goal of this proposed research is to develop, optimize and evaluate a new framework to model, acquire and process MRSI data to enable simultaneous, high-resolution, whole- brain mapping of metabolites and neurotransmitters in clinically feasible time. To achieve this goal, in Aim 1, we will design and implement a novel acquisition strategy that synergistically combines SNR- efficient, multi-slab and multi-TE excitation, sparse sampling in a (k,t,TE)-space and optimized TE selection with maximum echo sampling to generate J-resolved (multi-TE) MRSI data with an unprecedented combination of speed, resolution and organ coverage. In Aim 2, we will develop novel nonlinear low-dimensional models of general MR spectra using a learning-based strategy that integrates the biochemical priors of neural tissues, known physics-based MRSI signal modeling and deep neural networks. These learned models will effectively reduce the dimensionality of the imaging problem and allow for significantly improved speed, resolution and SNR tradeoffs as well as signal separation. Novel computational solutions that effectively exploit the learned models and other spatial-spectral-TE constraints will be developed for spatiospectral reconstruction of metabolites and neurotransmitters from the noisy, high-resolution J-resolved MRSI data. Finally, in Aim 3, we will systematically evaluate the proposed technology in terms of speed, resolution, SNR, and quantitative accuracy using computer simulations, phantom and in vivo experiments. The feasibility and robustness of the proposed technology for mapping metabolites and neurotransmitters in both healthy volunteers and temporal lobe epilepsy patients with mesial temporal sclerosis will be demonstrated. The success of the proposed research will lead to significant progress for in vivo MRSI and represent an important step towards the creation of a powerful tool for studying the molecular basis of brain functions and diseases. This tool, when fully developed, will add a transformative dimension to the existing neuroimaging technology profiles, with the potential to impact the diagnosis and management of neurological and neurodegenerative diseases.

项目总结/摘要 神经组织的代谢物和神经递质谱提供了一个独特的窗口，以了解大脑的 生理状态，并可用于提取潜在的生物标志物，用于检测和表征 神经退行性疾病磁共振波谱成像（MRSI）允许同时 许多代谢物和神经递质的映射和定量， 因此造影剂为脑的分子成像提供了巨大的机会。然而，在这方面， 由于几个基本的技术挑战，包括低SNR、差的空间分辨率、长时间 成像时间和光谱重叠分子信号的不准确分离，大多数体内MRSI 迄今为止的研究仍然局限于具有小大脑的非常低分辨率的实验（~ 1cm 3体素大小） 覆盖范围。这项研究的主要目标是开发、优化和评估一种新的 框架来建模，获取和处理MRSI数据，以实现同步，高分辨率，整体 在临床上可行的时间内绘制代谢物和神经递质的脑图谱。为了实现这一目标，在 目标1，我们将设计和实现一种新的捕获策略，协同结合信噪比- 有效的、多板和多TE激励、（k，t，TE）空间中的稀疏采样和优化的TE 选择最大回波采样，生成J分辨（多TE）MRSI数据， 前所未有的速度、分辨率和器官覆盖率。在目标2中，我们将开发新的 使用基于学习的策略的一般MR谱的非线性低维模型， 神经组织的生物化学先验、已知的基于物理学的MRSI信号建模和深层神经 网络.这些学习的模型将有效地降低成像问题的维度， 允许显著改进的速度、分辨率和SNR折衷以及信号分离。小说 计算解决方案，有效地利用学习模型和其他空间光谱TE 将为代谢物和神经递质的空间光谱重建制定约束条件， 噪声、高分辨率J分辨MRSI数据。最后，在目标3中，我们将系统地评估 提出的技术在速度，分辨率，信噪比和定量精度方面使用计算机 模拟、体模和体内实验。所提出的技术的可行性和鲁棒性 用于绘制健康志愿者和颞叶癫痫患者的代谢物和神经递质 颞叶内侧硬化症患者将被证明。这项研究的成功将 导致体内MRSI的重大进展，并代表了创建一个 研究大脑功能和疾病的分子基础的有力工具。该工具，当完全 开发，将增加一个变革性的层面，以现有的神经成像技术概况， 影响神经系统和神经退行性疾病的诊断和管理的潜力。