Development of Multi-Compartment MR-Fingerprinting for Subvoxel Estimation of Quantitative Tissue Biomarkers

用于定量组织生物标志物亚体素估计的多室 MR 指纹技术的开发

• 批准号: 10016296
• 负责人: Jakob Asslaender
• 金额: $ 25.22万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-11 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016296
• 关键词: 3D Print; Address; Behavior; Biological; Biological Markers; Cellular Structures; Cerebrospinal Fluid; Chemicals; Clinical; Complex; Computer-Assisted Diagnosis; Data; Data Set; Dementia; Demyelinations; Development; Diagnosis; Dictionary; Dimensions; Disease; Elements; Fingerprint; Goals; Image; Imaging Techniques; Lead; Learning; Liquid substance; Literature; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Modeling; Multicenter Studies; Multiple Sclerosis; Myelin; Neurodegenerative Disorders; Noise; Pathology; Performance; Phase; Physics; Physiologic pulse; Procedures; Protons; Relaxation; Reproducibility; Resolution; Scanning; Signal Transduction; Standardization; System; Techniques; Technology; Testing; Thick; Time; Tissues; Training; Validation; Variant; Water; base; clinical translation; conditioning; deep neural network; density; gray matter; healthy volunteer; image reconstruction; imaging system; improved; in vivo; magnetic resonance imaging biomarker; millimeter; neuroimaging; noninvasive diagnosis; novel; physical process; reconstruction; simulation; soft tissue; tissue biomarkers; white matter

Project Summary Magnetic resonance fingerprinting (MRF) has been proposed as a technique to quantify tissue parameters, such as T1 and T2 relaxation times, which are biomarkers for various pathologies. One assumption of MRF is that the signal in each voxel is generated by exactly one set of tissue parameters. Due to MRI resolution at the range of millimeter in combination with the cellular structure of biological tissue, each voxel consists of multiple tissue compartments. An over-simplified single compartment model results in apparent relaxation times that are influenced by the relaxation times and the fractional proton densities of all contributing compartments. This can lead to a misinterpretation of signal changes. For example, in diseases that causes demyelination in white matter (Multiple Sclerosis, Dementia), a reduction of the myelin water fraction would result in a misleading change of the apparent relaxation time of the voxel. We propose a multi-compartment MRF method that allows to identify multiple tissue contributions within a voxel, including the fractional proton density (PD) of different compartments. Our machine learning based approach automatically identifies the number of compartments within each voxel that can be identified with the available SNR in that voxel. We will correct for partial-volume effects at the borders of two types of tissues, as well as analyze tissue microstructure. For the second case our learned model will also include chemical exchange between compartments. After an initial validation phase using numerical simulations, we will first perform MRF scans of dedicated 3D printed phantoms with multiple compartments. Our quality criterion is successful estimation of all simulated tissue compartments for all voxels with a relative error of less than 5% to the ground truth. We will then perform in-vivo MRF measurements of healthy volunteers (n=5). We will generate synthetic FLAIR and MP-RAGE contrasts from parameter maps estimated with conventional and the proposed multi-compartment MRF technique. We will compare them with currently used clinical contrasts acquired using established pulse sequences and validate the performance of our approach by measuring the cortical thickness. Further, we will validate the performance for microstructure composition in white matter. Our hypothesis is that it will be possible separate the compartments for myelin, intra- and extra-cellular water and compare the results to ex-vivo data found in literature. In summary, the methods developed in this R21 proposal will provide a novel technique to accurately and reproducibly identify biomarkers beyond the resolution of a voxel. It will allow to identify changes in tissue composition and fractional proton density at the microstructure level.

项目摘要 磁共振指纹(MRF)已经被提出作为一种量化组织参数的技术，例如 作为T1和T2松弛时间，这是各种病理的生物标志物。MRF的一个假设是 每个体素中的信号恰好由一组组织参数产生。由于MRI分辨率在 毫米结合生物组织的细胞结构，每个体素由多个组织组成 车厢。过度简化的单室模型导致表观松弛时间为 受弛豫时间和所有贡献格室的分数质子密度的影响。这可以 导致对信号变化的误解。例如，在导致白质脱髓鞘的疾病中 (多发性硬化症，痴呆症)，髓鞘水分的减少会导致误导性的改变 体素的外观松弛时间。 我们提出了一种多间隔MRF方法，其允许识别体素内的多个组织贡献， 包括不同隔室的分数质子密度(Pd)。我们基于机器学习的方法 自动识别每个体素内可用可用 该体素中的信噪比。我们将校正两种类型组织边界的部分体积效应，以及 分析组织微结构。对于第二种情况，我们学习的模型也将包括化学交换 在车厢之间。 在使用数值模拟进行初始验证阶段后，我们将首先执行专用3D的MRF扫描 带有多个隔间的印刷幻影。我们的质量标准是成功估计所有模拟组织 所有体素的隔室，与地面实况的相对误差小于5%。然后我们将在体内进行 健康志愿者(n=5)的MRF测量。我们将产生人工合成的天赋和MP-RAGE的对比 从用常规和建议的多间隔磁流变液技术估计的参数图。我们会 将它们与使用已建立的脉冲序列获取的当前使用的临床对比进行比较并验证 通过测量皮质厚度来测试我们方法的性能。此外，我们将对性能进行验证 用于白质中的显微结构组成。我们的假设是，有可能将 对髓鞘、细胞内和细胞外的水进行检测，并将结果与在 文学。 总之，本R21提案中开发的方法将提供一种新的技术，以准确和 可重复地识别超出体素分辨率的生物标志物。它将允许识别组织的变化 微结构水平上的组成和分数质子密度。