Deep-Learning-Augmented Quantitative Gradient Recalled Echo (DLA-qGRE) MRI for in vivo Clinical Evaluation of Brain Microstructural Neurodegeneration in Alzheimer Disease

阿尔茨海默病(AD)是美国和全世界的主要健康问题之一；它是一种神经退行性疾病 一种以脑组织病变引起的进行性痴呆为临床特征的疾病 比临床症状提前15-20年。临床上可获得的方法是筛查的关键 早期AD病理和随时间的监测，以及在临床药物试验中的结果衡量。 这项拨款申请的目标是建立一种基于核磁共振的技术，深度学习-增强 定量梯度回声(DLA-qGRE)作为脑组织定量临床评价的平台 阿尔茨海默病(AD)临床前早期的微结构神经变性。DLA-qGRE是一种 QGRE MRI技术与去伪影正则化深度学习相结合 方法论，都是由我们团队开发的。QGRE数据来自一组具有良好特征的患者 显示存在低R2t*值的脑区(暗物质)，代表组织基本上缺失 神经细胞的。这些数据表明，在阿尔茨海默病临床前阶段的患者中，已经可以识别出暗物质 (淀粉样阳性但没有临床症状)，并且对未来的AD进展也有预测能力。 虽然qGRE序列可以在任何商用MRI扫描仪上实现，但目前数据分析 需要数小时的计算时间，调整临床应用程序。显著加速和改进数据 分析以及数据获取，在此提案中，我们将使用创新的REARE技术，即 明确说明了特定成像系统的物理模型和生物的生物物理模型 纸巾。初步数据显示，DL具有在几秒钟内重建qGRE指标的潜力 提高了图像质量，降低了噪音。这为广泛实施DLA-qGRE提供了机会 可用于临床应用的工具。在此基础上，我们计划实现以下具体目标： 在目标1中，我们将开发与商业MRI协议兼容的DLA-qGRE数据处理流水线 可用的GRE序列，用于快速可靠地检测微结构萎缩前神经变性。 在目标2中，我们将优化k空间采样策略来开发qGRE成像协议 分辨率各向同性，同时缩短了MRI采集时间。减少扫描时间将显著 帮助患者舒适，不太容易受到运动的影响，并降低核磁共振检查的成本。 在目标3中，我们将在临床神经放射学设置中证明DLA-qGRE与MRI方案兼容 商业上可用的GRE序列(按目标1开发)和加速的DLA-qGRE(按目标1开发 目的2)能可靠地检测临床前和早期症状性阿尔茨海默病患者的微结构神经变性。 综上所述，本方案的目标的成功完成将为DLA-qGRE在临床上的应用打开大门 作为神经退行性变的新的、更敏感和更特异的MRI诊断方法的设置 AD早期病理方面与目前对组织萎缩的测量相比较。

Alzheimer Disease (AD) is one of the major health problems in the US and worldwide; it is a neurodegenerative disorder that is characterized clinically by progressive dementia caused by pathological changes in brain tissue preceding clinical symptoms by 15-20 years. Clinically-accessible methods are critically needed to screen for early AD pathology and monitoring it over time, as well as for outcome measures in clinical drug trials. The goal of this grant application is to establish an MRI-based technique, Deep-Learning-Augmented quantitative Gradient Recalled Echo (DLA-qGRE), as a platform for quantitative clinical evaluation of brain tissue microstructural neurodegeneration at early preclinical stages of Alzheimer Disease (AD). DLA-qGRE is a combination of qGRE MRI technique and Regularization by Artifact REmoval (RARE) deep learning (DL) methodology, both developed by our team. qGRE data obtained from a well-characterized cohort of patients revealed the existence of brain regions with low R2t* values (Dark Matter), representing tissue essentially devoid of neurons. These data show that Dark Matter can be identified already in people with preclinical stages of AD (amyloid positive but without clinical symptoms) and also has a predictive power of future AD progression. While qGRE sequence can be implemented on any commercial MRI scanner, the data analysis currently requires hours of computing time, tempering clinical applications. To significantly accelerate and improve data analysis, as well as data acquisition, in this proposal we will use innovative RARE technique, a DL approach that explicitly accounts for the physical models of specific imaging systems and biophysical models of biological tissues. Preliminary data show that DL has a potential for reconstructing qGRE metrics in a matter of seconds with improved image quality and reduced noise. This opens opportunity for implementing DLA-qGRE as a widely available tool for clinical applications. Based on this approach, we plan to achieve the following Specific Aims: In Aim 1 we will develop DLA-qGRE data processing pipeline, compatible with MRI protocols of commercially available GRE sequences, for fast and reliable detection of microstructural pre-atrophic neurodegeneration. In Aim 2 we will optimize k-space sampling strategy for developing qGRE imaging protocol with increased isotropic resolution and simultaneously decreased MRI acquisition time. Reducing scan time will significantly help with patient comfort, be much less susceptible to motion, and reduce costs of the MRI exam. In Aim 3 we will demonstrate that in a clinical neuroradiology setting DLA-qGRE compatible with MRI protocols of commercially available GRE sequences (developed per Aim 1), and accelerated DLA-qGRE (developed per Aim 2), can reliably detect microstructural neurodegeneration in preclinical and early symptomatic AD. In Summary, successful completion of the aims of this proposal will open doors for using DLA-qGRE in clinical settings as novel and more sensitive and specific MRI-based diagnostic measure of the neurodegenerative aspects of early AD pathology as compared with current measurements of tissue atrophy.