Simultaneous Multinuclear Magnetic Resonance Fingerprinting for Data Fusion of Quantitative Structural and Metabolic Imaging

用于定量结构和代谢成像数据融合的同步多核磁共振指纹图谱

• 批准号: 9889957
• 负责人: Guillaume MADELIN
• 金额: $ 64.7万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9889957
• 关键词: 3-Dimensional; Affect; Age; Algorithms; Anterior; Architecture; Back; Biochemistry; Blood Circulation; Brain; Brain Ischemia; Cerebrospinal Fluid; Chronic; Clinical; Computer software; Data; Development; Diffusion; Disease; Ensure; Exhibits; Extracellular Fluid; Fingerprint; Frequencies; Gender; Goals; Homeostasis; Human; Image; Imaging Techniques; Intracellular Fluid; Ions; MRI Scans; Magnetic Resonance; Maps; Measurable; Measurement; Metabolic; Metabolism; Methods; Minor; Modality; Modeling; Monitor; Morphology; Nature; Nerve Degeneration; Neurocognitive Deficit; Patients; Perfusion; Physiologic pulse; Play; Process; Property; Protocols documentation; Protons; Recurrence; Reproducibility; Resolution; Scanning; Signal Transduction; Sodium; Stroke; Structure; Techniques; Testing; Time; Tissue Model; Tissues; Training; Transient Ischemic Attack; Validation; base; clinical application; computerized data processing; data acquisition; data fusion; density; design; extracellular; flexibility; hemodynamics; in vivo; insight; learning strategy; metabolic abnormality assessment; metabolic imaging; models and simulation; molecular imaging; new technology; non-invasive imaging; novel; prototype; radio frequency; reconstruction; simulation; statistical learning

Project Summary In this project we want to develop a new non-invasive imaging technique that will provide multi-parametric metabolic maps of the living brain at an unprecedented resolution. The key to this new technology is the novel combination of three state-of-the-art imaging concepts: (A) new hardware that enables the simultaneous measurement of multinuclear magnetic resonance (MR) signals at different frequencies; (B) the flexibility and robustness of Plug-and-Play (PnP) MR Fingerprinting (MRF); and (C) a data fusion process driven by a cross-modality model based on statistical learning. For brevity, we will call this fused simultaneous multinuclear PnP-MRF technique MNF (Multi-Nuclear Fusion). The idea behind MNF is to rapidly capture two different kinds of quantitative information throughout the whole brain in one single scan: (1) structural information from proton (1H) MRF such as T1, T2, and proton density (PD) (tissue-scale morphology); and (2) metabolic information related to ion homeostasis from sodium (23Na) MRF, such as intracellular sodium concentration, and intracellular, extracellular and cerebrospinal fluid (CSF) volume fractions (cellular-scale function). Because PnP-MRF can quantify multiple tissue properties free of experimental bias, it enables us to employ statistical learning to discover a subject-specific cross-modality model that integrates all voxelwise inter-relationships between the multi-parametric 1H PnP-MRF (acquired at high resolution, 0.75-1 mm) and 23Na PnP-MRF (acquired at low resolution, 3-5 mm) maps. These subject-specific relations can subsequently be used to sharpen the 23Na metabolic maps to match the resolution of the 1H structural maps. The high-resolution 23Na maps will enable the assessment of metabolic processes in vivo and bridge the gap in resolution that has held back our ability to study metabolism in the living human brain, which is crucial for our understanding of the brain itself and the afflictions that affect it. This proof-of-concept implementation will be developed at 7 T, but it is expected to be adaptable to clinical 3 T MR scanners. The specific aims are: (1) Data acquisition, (1.a) multi-channel 1H/23Na RF array, (1.b) simultaneous multinuclear 3D MRF sequence; (2) Data processing, (2.a) PnP-MRF reconstruction for both 1H data (fingerprint matching to generate structural maps) and 23Na data (tissue 4-compartment model and simulation of spin 3/2 dynamics to generate metabolic maps), (2.b) cross-modality model using statistical learning, and data fusion algorithm to generate high-resolution metabolic maps; (3) Method validation, (3.a) accuracy and precision, (3.b) repeatability and reproducibility.(3) Exploratory aim: Test MNF on patients with chronic steno-occlusive disease, with recurrent transient ischemic attacks (TIA)/minor stroke, presenting regional brain ischemia, at 3 time points (baseline, 8-month and 16-month follow-ups), and comparison with healthy controls.

项目摘要 在这个项目中，我们希望开发一种新的非侵入性成像技术，它将提供多参数 以前所未有的分辨率绘制活体大脑的新陈代谢图谱。这项新技术的关键是小说 结合了三个最先进的成像概念：(A)支持同时 多核磁共振(MR)信号在不同频率的测量；(B)灵活性和稳健性 即插即用(PnP)磁共振指纹(MRF)；以及(C)由跨通道模型驱动的数据融合过程 基于统计学习。为简洁起见，我们将这种融合的同时多核PNP-MRF技术称为 多核聚变。MNF背后的想法是快速捕获两种不同类型的量化 一次扫描整个大脑的信息：(1)来自质子(1H)磁共振波谱的结构信息，如 T1、T2和质子密度(PD)(组织尺度形态)；以及(2)与离子有关的代谢信息 钠(23Na)磁流变液的动态平衡，如细胞内钠浓度，以及细胞内、细胞外和 脑脊液(CSF)体积分数(细胞尺度功能)。因为PNP-MRF可以量化多个 没有实验偏见的组织特性，它使我们能够使用统计学习来发现特定于对象的 集成多参数1H PNP-MRF之间所有体素相互关系的跨通道模型 23Na PNP-MRF图(低分辨率，3-5 mm)。这些 随后可以使用特定对象的关系来锐化~(23)Na代谢图以匹配分辨率 1H结构图。高分辨率的~(23)Na图将能够评估 并弥合分辨率上的差距，这一差距阻碍了我们研究活着的人脑新陈代谢的能力， 这对我们理解大脑本身和影响它的痛苦至关重要。这一概念验证 实施将在7T开发，但预计将适用于临床3T磁共振扫描仪。这个 具体目标是：(1)数据采集，(1.a)多通道1H/23Na射频阵列，(1.b)同时多核 3DMRF序列；(2)数据处理；(2.a)两个1H数据的PNP-MRF重建(指纹匹配 以生成结构图)和23Na数据(组织四室模型和自旋3/2动力学模拟 生成代谢图)，(2.b)使用统计学习的跨通道模型，以及数据融合算法 生成高分辨率代谢图；(3)方法验证；(3.a)准确性和精密度；(3.b)重复性 和重复性。(3)探索性目标：检测慢性狭窄闭塞症患者的MNF， 复发性短暂性脑缺血发作(TIA)/轻微中风，表现为局部脑缺血，在3个时间点(基线， 8个月和16个月的随访)，并与健康对照组进行比较。