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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10659833
• 关键词: 3-Dimensional; Acceleration; Age; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnostic; Alzheimer' s disease pathology; Alzheimer' s disease test; Amyloid; Applications Grants; Architecture; Atrophic; Biological; Biological Models; Biophysics; Brain; Brain Diseases; Brain region; Clinical; Computer software; Darkness; Data; Data Analyses; Dementia; Detection; Development; Diagnostic; Diagnostic tests; Disease Progression; Excision; Future; Goals; Health; Hour; Image; Intervention; Least-Squares Analysis; Magnetic Resonance Imaging; Manufacturer; Maps; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Noise; Outcome Measure; Participant; Pathologic; Patients; Pattern; Persons; Pharmaceutical Preparations; Physiologic pulse; Population; Predisposition; Procedures; Property; Protocols documentation; RF coil; Research; Resolution; Sampling; Scanning; Sensitivity and Specificity; Signal Transduction; Symptoms; System; Techniques; Testing; Time; Tissues; Training; Translating; biophysical model; brain tissue; clinical application; cohort; computerized data processing; cost; dark matter; data acquisition; data analysis pipeline; deep learning; early detection biomarkers; early screening; hemodynamics; high risk; image reconstruction; imaging approach; imaging system; improved; in vivo; indexing; innovation; magnetic field; neuropathology; novel; physical model; pre-clinical; prevent; reconstruction; research clinical testing; sex; structural imaging; tool

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.

阿尔茨海默氏病（AD）是美国和全球的主要健康问题之一。这是一种神经退行性 由脑组织病理学变化引起的临床表征的疾病 在15 - 20年之前出现临床症状。临床上可访问的方法非常需要筛选 早期的AD病理学并随着时间的流逝以及临床药物试验中的结果指标进行监测。 该赠款申请的目的是建立基于MRI的技术，深入学习 定量梯度召回回声（DLA-QGRE），作为脑组织定量临床评估的平台 阿尔茨海默氏病早期临床前阶段（AD）的微结构神经变性。 Dla-qgre是一个 QGRE MRI技术和正规化通过伪影（稀有）深度学习（DL）组合 方法论，都是由我们的团队开发的。 QGRE数据从特征良好的患者队列获得 揭示了R2T*值低（暗物质）的大脑区域的存在，代表组织本质上是无孔的 神经元。这些数据表明，可以在AD的临床前阶段确定暗物质 （淀粉样蛋白阳性，但没有临床症状），并且还具有未来AD进展的预测能力。 虽然QGRE序列可以在任何商业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，调速临床应用。显着加速和改善数据 分析以及数据获取，在此提案中，我们将使用创新的稀有技术，这是一种DL方法 明确说明特定成像系统和生物物理模型的物理模型 组织。初步数据表明，DL有可能在几秒钟内重建QGRE指标 图像质量的提高和噪音降低。这为实施DLA-QGRE的机会打开了机会 可用于临床应用的工具。基于这种方法，我们计划实现以下特定目标： 在AIM 1中，我们将开发DLA-QGRE数据处理管道，与商业上的MRI协议兼容 可用的GRE序列，用于快速和可靠的微结构前神经变性。 在AIM 2中，我们将优化K空间采样策略，以增强QGRE成像协议 各向同性分辨率和同时减少了MRI获取时间。减少扫描时间将大大显着 帮助患者舒适，不太容易受到运动的影响，并降低MRI考试的成本。 在AIM 3中，我们将证明在临床神经放射学中，DLA-QGRE与MRI方案兼容 商业可用的GRE序列（每个AIM 1）和加速DLA-QGRE（开发 AIM 2），可以可靠地检测到临床前和早期症状广告中的微结构神经退行性。 总而言之，该提案的成功完成将为使用DLA-QGRE打开临床的门 设置为神经退行性的新颖，更灵敏，更特定的基于MRI的诊断度量 与组织萎缩的当前测量相比，早期AD病理学的各个方面。