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

深度学习增强定量梯度回忆回波 (DLA-qGRE) MRI 用于阿尔茨海默病脑微结构神经变性的体内临床评估

基本信息

批准号：
10659833
负责人：
Manu S Goyal
金额：
$ 199.18万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-15 至 2026-02-28
项目状态：
未结题

项目摘要

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.

阿尔茨海默病（AD）是美国和全世界的主要健康问题之一；这是一种神经退行性疾病临床特征为由脑组织病理变化引起的进行性痴呆的疾病提前临床症状 15-20 年。迫切需要临床可用的方法来筛查早期 AD 病理学并随着时间的推移对其进行监测，以及临床药物试验中的结果测量。本次拨款申请的目标是建立一种基于 MRI 的技术，即深度学习增强技术定量梯度回忆回波（DLA-qGRE），作为脑组织定量临床评估的平台阿尔茨海默病（AD）早期临床前阶段的微观结构神经变性。 DLA-qGRE 是 qGRE MRI 技术与伪影去除正则化 (RARE) 深度学习 (DL) 的结合方法论，均由我们团队开发。 qGRE 数据从特征明确的患者队列中获得揭示了 R2t* 值较低的大脑区域（暗物质）的存在，代表组织基本上没有神经元。这些数据表明，在 AD 临床前阶段的人群中已经可以识别出暗物质（淀粉样蛋白阳性但无临床症状）并且还具有预测未来 AD 进展的能力。虽然 qGRE 序列可以在任何商用 MRI 扫描仪上实施，但目前的数据分析需要数小时的计算时间，以适应临床应用。显着加速和改进数据分析以及数据采集，在本提案中，我们将使用创新的 RARE 技术，这是一种 DL 方法，明确地解释了特定成像系统的物理模型和生物的生物物理模型组织。初步数据显示 DL 有可能在几秒钟内重建 qGRE 指标提高图像质量并降低噪声。这为将 DLA-qGRE 广泛实施提供了机会临床应用的可用工具。基于这种方法，我们计划实现以下具体目标：在目标 1 中，我们将开发 DLA-qGRE 数据处理管道，与商业 MRI 协议兼容可用的 GRE 序列，用于快速可靠地检测微观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，用于开发 qGRE 成像协议，增加各向同性分辨率，同时减少 MRI 采集时间。显着减少扫描时间有助于提高患者的舒适度，减少运动的影响，并降低 MRI 检查的成本。在目标 3 中，我们将证明在临床神经放射学环境中 DLA-qGRE 与 MRI 协议兼容市售 GRE 序列（根据目标 1 开发）和加速 DLA-qGRE（根据目标 1 开发）目标 2)，能够可靠地检测临床前和早期症状性 AD 中的微观结构神经变性。总之，成功完成该提案的目标将为在临床中使用 DLA-qGRE 打开大门设置作为神经退行性疾病的新颖且更敏感和更具体的基于 MRI 的诊断措施与目前组织萎缩的测量结果相比，早期 AD 病理学的各个方面。