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.

项目总结