Quantitative MRI of Multiple Sclerosis - Resubmission - 1

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， 加权MR图像的映射。磁共振成像（MRI）通常用作诊断工具 多发性硬化（MS）和跟踪病变随时间的变化。因为核磁共振成像有各种对比度， 关于底层组织微观结构和生理学的不同信息，它可以潜在地用作 预测MS疾病进展甚至残疾的工具。然而，没有已知的措施， MS的MR图像与扩展残疾状态评分描述的临床残疾密切相关 （EDSS）。先前将MRI特征与EDSS相关联的努力包括计算T1上的总病变负荷， T_2加权像，测量正常脑磁转移率的变化 组织，计算脑萎缩，每一个都有不同程度的成功。然而，很少有人研究 病变弛豫时间随时间的演变及其与残疾的关系。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中的病理变化 随着时间的推移，从MR图像导出的定量T1和T2图可能更能指示微观变化 表现为MS患者的残疾。 本项目的具体目标是：（1）开发和验证新的T1和T2定量方法， 自旋回波MR图像，（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，以使用机器学习来预测EDSS。目标1将涉及 在体内T1和T2加权自旋回波MR图像上验证量化管道， 在新的定量方法的可接受的参数范围内。目标2将扩展量化 管道，以包括常用的和更复杂的MS成像序列，再次导致一系列的 定量方法的可接受参数。AIM 3将使用量化管道进行比较 有量化和没有量化的机器学习算法，以确定量化在 此外，Aim 3将产生利用多个成像的预测机器学习模型。 这些结果将为MS中残疾的预测提供更透彻的理解， MR量化在神经系统疾病（如MS）评估中的作用，并将提供科学的 为扩展MR定量作为其他疾病的潜在成像生物标志物的使用奠定了基础， 病理学

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.