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.

项目摘要/摘要 神经疗法的代谢物和神经递质轮廓为大脑的独特窗口提供了独特的窗口 物理状态，可用于提取潜在的生物标志物来检测和表征 神经退行性疾病。磁共振光谱成像（MRSI）允许同时进行 许多代谢物和神经递质的映射和定量没有外源 因此，对比剂承诺为大脑的分子成像带来巨大的机会。然而， 由于几个基本的技术挑战，包括低SNR，空间分辨率差，很长 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仅限于非常低分辨率的实验（〜1cm3 Voxel大小） 覆盖范围。这项拟议研究的主要目标是开发，优化和评估新的 建模，获取和处理MRSI数据的框架以实现简单，高分辨率，整体 在临床上可行的时间内代谢物和神经递质的脑图。为了实现这一目标， AIM 1，我们将设计和实施一种新颖的采集策略，从而协同结合SNR- 在A（k，t，te）空间和优化的TE中，有效，多斜线和多TE的兴奋，稀疏采样 使用最大回声采样的选择以生成J分解（多TE）MRSI数据 速度，分辨率和器官覆盖率的前所未有的组合。在AIM 2中，我们将开发小说 使用基于学习的策略的一般MR光谱的非线性低维模型 神经疗法的生化先验，已知的基于物理学的MRSI信号建模和深神经检查 网络。这些学习的模型将有效地降低成像问题的维度和 允许显着提高速度，分辨率和SNR权衡以及信号分离。小说 有效探索学习模型和其他空间光谱-TE的计算解决方案 将开发出限制因素，用于从事代谢物和神经递质从 噪声，高分辨率J分解的MRSI数据。最后，在AIM 3中，我们将系统地评估 在使用计算机的速度，分辨率，SNR和定量准确性方面提议的技术 模拟，幻影和体内实验。拟议技术的可行性和鲁棒性 用于映射健康志愿者和临时叶癫痫的代谢物和神经递质 将证明患有介内暂时性硬化症的患者。拟议研究的成功将 导致体内MRSI取得重大进展，并代表了创建一个重要的一步 研究大脑功能和疾病的分子基础的强大工具。该工具完全 开发的，将为现有的神经影像技术概况增加一个变革性的维度 影响神经和神经退行性疾病的诊断和管理的潜力。