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.

项目概要/摘要 神经组织的代谢物和神经递质概况提供了了解大脑的独特窗口 生理状态，可用于提取潜在的生物标志物以进行检测和表征 磁共振波谱成像 (MRSI) 可以同时检测神经退行性疾病。 无需外源性即可对多种代谢物和神经递质进行绘图和定量 因此，造影剂为大脑的分子成像提供了巨大的机会。 由于几个基本的技术挑战，包括信噪比低、空间分辨率差、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大脑较小的极低分辨率实验（~1cm3 体素大小） 这项研究的主要目标是开发、优化和评估新的覆盖范围。 用于建模、采集和处理 MRSI 数据的框架，以实现同步、高分辨率、整体 在临床可行的时间内绘制代谢物和神经递质的大脑图谱以实现这一目标。 目标 1，我们将设计并实施一种新颖的采集策略，该策略协同结合 SNR- 高效、多板和多 TE 激励、(k,t,TE) 空间中的稀疏采样和优化的 TE 选择最大回波采样来生成 J 分辨（多 TE）MRSI 数据 在目标 2 中，我们将开发新颖的速度、分辨率和器官覆盖范围。 使用基于学习的策略集成一般 MR 谱的非线性低维模型 神经组织的生化先验、已知的基于物理的 MRSI 信号建模和深度神经 这些学习模型将有效地降低成像问题的维度和 允许显着提高速度、分辨率和信噪比权衡以及新颖的信号分离。 有效利用学习模型和其他空间光谱 TE 的计算解决方案 将为代谢物和神经递质的空间谱重建制定约束条件 最后，在目标 3 中，我们将系统地评估噪声、高分辨率 J 分辨 MRSI 数据。 使用计算机在速度、分辨率、信噪比和定量精度方面提出的技术 模拟、模型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图谱 将证明拟议研究的成功。 导致体内 MRSI 取得重大进展，并代表着朝着创建 研究大脑功能和疾病的分子基础的强大工具。 开发的，将为现有的神经影像技术概况添加变革性的维度， 影响神经系统和神经退行性疾病的诊断和治疗的潜力。