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.

阿尔茨海默病（AD）是美国和世界范围内的主要健康问题之一;它是一种神经退行性疾病， 一种临床上以脑组织病理变化引起的进行性痴呆为特征的疾病 出现临床症状前15-20年。迫切需要临床上可获得的方法来筛查 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。 这项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。 定量梯度回波（DLA-qGRE），作为脑组织定量临床评价的平台 阿尔茨海默病（AD）早期临床前阶段的微结构神经变性。DLA-qGRE是一个 结合qGRE MRI技术和RARE深度学习（DL）正则化 这两种方法都是我们团队开发的。从充分表征的患者队列中获得的qGRE数据 揭示了存在具有低R2 t * 值（暗物质）的大脑区域，代表基本上没有 的神经元。这些数据表明，暗物质已经可以在AD临床前阶段的人中被识别出来。 （淀粉样蛋白阳性但无临床症状）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扫描仪上实现，但目前的数据分析 需要数小时的计算时间，锻炼临床应用。显著加快和改善数据 分析，以及数据采集，在这个建议中，我们将使用创新的RARE技术，一种DL方法， 明确说明了特定成像系统的物理模型和生物成像系统的生物物理模型。 组织中初步数据显示，DL有潜力在几秒钟内重建qGRE指标 具有改善的图像质量和降低的噪声。这为将DLA-qGRE作为广泛的 可用于临床应用。基于这一方法，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数据处理管道 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，以开发qGRE成像协议，增加 各向同性分辨率和同时减少的MRI采集时间。缩短扫描时间将显著 有助于患者舒适度，更不易受运动影响，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设置中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 目的2），可以可靠地检测临床前和早期症状性AD的微结构神经变性。 总之，成功完成本提案的目标将为在临床上使用DLA-qGRE打开大门。 作为一种新的、更敏感和更特异的基于MRI的神经退行性疾病的诊断措施， 早期AD病理学方面与目前的组织萎缩测量相比。