Quantitative MRI of Multiple Sclerosis - Resubmission - 1

多发性硬化症的定量 MRI - 重新提交 - 1

• 批准号: 10316992
• 负责人: Adam James Hasse
• 金额: $ 4.57万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10316992
• 关键词: Affect; Age-Years; Attenuated; Axon; Brain; Cerebrospinal Fluid; Clinical; Coupled; Data; Data Set; Demyelinations; Dependence; Development; Diagnosis; Disease; Disease Progression; Ensure; Equation; Evaluation; Evolution; Fatty acid glycerol esters; Foundations; Frequencies; Image; Inflammation; Investigation; Learning; Lesion; Liquid substance; Literature; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Magnetism; Maps; Measures; Medical; Methods; Microscopic; Modeling; Morbidity - disease rate; Multiple Sclerosis; Neuraxis; Pathologic; Pathology; Patient imaging; Patients; Physiology; Population; Prediction of Response to Therapy; Predictive Value; Process; ROC Curve; Recovery; Reference Values; Relaxation; Research; Role; Scanning; Signal Transduction; T2 weighted imaging; Techniques; Time; Tissues; United States; Validation; Variant; brain tissue; cerebral atrophy; classification algorithm; contrast enhanced; contrast imaging; data harmonization; digital; disability; gray matter; healthy volunteer; imaging biomarker; improved; in vivo; in vivo imaging; longitudinal dataset; machine learning algorithm; multiple sclerosis patient; nervous system disorder; novel; predictive modeling; prospective; simulation; subcutaneous; success; tool; treatment response; white matter

Project Summary In this project, we propose a novel T1 and T2 quantification method that generates quantitative T1 or T2 maps from weighted MR images. Magnetic resonance imaging (MRI) is commonly used as a tool to diagnose Multiple Sclerosis (MS) and track lesional changes over time. Because MRI has various contrasts that display different information about the underlying tissue microstructure and physiology, it can potentially be used as a tool to predict MS disease progression and even disability. However, there is no known measure derived from MR images of MS that correlates well with clinical disability as described by the Expanded Disability Status Score (EDSS). Previous efforts to correlate MRI features and EDSS have included calculating total lesion load on T1- and T2-weighted images, measuring the variations in the magnetic transfer ration of normal-appearing brain tissues, and calculating cerebral atrophy, each with a varying level of success. Yet, there has been little study of the evolution of relaxation times of the lesions over time and how it relates to disability. Because changes in the T1 (spin-lattice) and T2 (spin-spin) relaxation times of a tissue can reflect pathological changes in that tissue over time, quantitative T1 and T2 maps derived from MR images may be more indicative of microscopic changes that manifest as disability in MS patients. The specific aims of this proposal are: (1) develop and validate novel T1 and T2 quantification method on spin-echo MR images, (2) extend the novel quantification method to common MS imaging sequences, and (3) apply the novel quantification method to MS datasets to predict EDSS using machine learning. Aim 1 will involve the validation of the quantification pipeline on both T1- and T2-weighted spin-echo MR images in vivo, resulting in a range of acceptable parameters for the novel quantification method. Aim 2 will extend the quantification pipeline to include commonly used and more complicated MS imaging sequences, again resulting in a range of acceptable parameters for the quantification method. Aim 3 will use the quantification pipeline to compare machine learning algorithms with and without quantification to determine the added value of quantification in the imaging of MS. Additionally, Aim 3 will result in a predictive machine learning model utilizing multiple imaging contrasts for the prediction of disability in MS. These results will provide a more thorough understanding of the role of MR quantification in the evaluation of neurological diseases, such as MS, and will offer a scientific foundation to extend the use of MR quantification as a potential imaging biomarker for other diseases and pathologies.

项目摘要 在这个项目中，我们提出了一种新的T1和T2量化方法，以生成定量的T1或T2 来自加权MR图像的地图。磁共振成像(MRI)通常被用作诊断 多发性硬化症(MS)和跟踪皮损随时间的变化。因为核磁共振成像有不同的对比度 关于潜在组织微结构和生理的不同信息，它可能被用作 预测多发性硬化症疾病进展甚至残疾的工具。然而，目前还没有已知的从 多发性硬化症的磁共振图像与扩展的残疾状态评分所描述的临床残疾有很好的相关性 (EDSS)。以前将MRI特征与EDSS相关联的努力包括计算T1- 和T2加权图像，测量正常大脑的磁转移比的变化 组织和计算大脑萎缩，每一个都有不同程度的成功。然而，几乎没有人研究过 病变的松弛时间随时间的演变及其与残疾的关系。这是因为 组织的T1(自旋-晶格)和T2(自旋-自旋)弛豫时间可以反映该组织的病理变化 随着时间的推移，从MR图像得出的定量T1和T2图可能更能指示微观变化 在MS患者中表现为残疾。 该方案的具体目标是：(1)开发和验证新的T1和T2量化方法 自旋回波磁共振成像，(2)将新的量化方法扩展到常见的MS成像序列，以及(3) 将新的量化方法应用于MS数据集，利用机器学习来预测EDSS。目标1将涉及 在体内对T1和T2加权自旋回波磁共振图像的量化流水线的验证，结果 在新的量化方法的可接受的参数范围内。目标2将扩大量化范围 包括常用和更复杂的MS成像序列的流水线，再次导致一系列 量化方法的可接受参数。目标3将使用量化管道进行比较 有量化和无量化的机器学习算法确定量化的附加值 此外，AIM 3将使用多个成像技术建立预测性机器学习模型 对多发性硬化症残疾预测的对比这些结果将提供更全面的理解 MR定量在评估神经系统疾病中的作用，如MS，将提供科学的 扩大MR量化作为其他疾病潜在成像生物标记物的使用 病理学。