Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI

项目概要 阿尔茨海默病和相关痴呆症是一场日益严重的公共卫生危机，影响着 580 万美国人，但 FDA 批准的治疗阿尔茨海默病的药物只有四种，其中没有一种能够缓解疾病。 因此，早期检测和诊断是成功患者管理的关键，并且需要生物标志物 在临床试验中评估新疗法。白质变化与早期阿尔茨海默病的关系越来越密切 疾病进展和扩散加权磁共振成像 (DW-MRI) 已被纳入许多研究中 国家规模的研究。然而，DW-MRI 数据的定量研究因缺乏一致性而受到阻碍 采集协议、站点和扫描仪的变化。 DW-MRI 可量化大脑微观结构 并促进结构连接映射。最近在校准和 协调以减少主体间差异并提高计算测量的可解释性。然而， 根本挑战仍然是 DW-MRI（目前实施的）的临床应用 被扫描仪间和站点间的影响所混淆。 为了加深对阿尔茨海默病结构变化的理解，我们将构建并评估三个 单独的分析策略来表征、校准和优化单受试者生物标志物的 DW-MRI 阿尔茨海默病的发展。我们将利用大型回顾来整合和优化我们的策略 多地点研究并在两个不同的前瞻性队列中验证这些方法。具体来说，我们的目标是： 目标 1：优化数据驱动技术，以实现跨会话、扫描仪/站点和场强的稳定性 影响：统一的 DW-MRI 方法将提高对阿尔茨海默病及其前驱阶段的敏感性。 目标 2：将微观结构协调的创新转化为结构连通性（纤维束成像） 影响：协调结构连接将提高对阿尔茨海默病白质的理解。 目标 3：通过规范的数据库建设，推进单主体推理的统计工具 影响：用于不确定性估计的数据驱动资源将实现稳健的单一主题推理。 对医疗保健的相关性和影响：拟议的研究将增进对阿尔茨海默病的了解 通过 (1) DW-MRI 生物标志物的定量协调，(2) 协调协议来治疗疾病 回顾性和前瞻性 DW-MRI 研究，以及 (3) 针对老年人的单受试者推理新工具 队列。我们将组织研讨会/挑战赛，以最大限度地提高对临床科学的转化影响。的 我们研究的长期目标是 (1) 提供一种经过充分验证的策略来定量评估 DW-MRI 数据 跨站点，(2) 增强阿尔茨海默病的 DW-MRI 生物标志物，(3) 促进患者护理。我们的 研究策略将改变 DW-MRI 数据的解释方式并实现单一受试者 机器学习来解释大脑特性。将制作资源、软件和可视化工具 通过 DIPY 以开源方式免费提供，以促进持续创新。

PROJECT SUMMARY Alzheimer’s Disease and related dementia are a growing public health crisis affecting 5.8 million Americans, yet there are only four FDA-approved medications for Alzheimer’s Disease, none of which are disease-modifying. Hence, early detection and diagnosis are key to successful patient management and biomarkers are needed for evaluating new therapies in clinical trials. White matter changes are increasingly implicated in early Alzheimer’s Disease progression, and diffusion weighted magnetic resonance imaging (DW-MRI) has been included in many national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variation in acquisition protocols, sites, and scanners. DW-MRI enables quantification of brain microstructure and facilitates structural connectivity mapping. Substantial recent progress has been made with calibration and harmonization to reduce inter-subject variance and improve interpretability of computed measures. Yet, the fundamental challenge remains that clinical application of DW-MRI (as currently implemented) is confounded by inter-scanner and inter-site effects. To improve understanding of structural changes in Alzheimer’s Disease, we will construct and evaluate three separate analysis strategies to characterize, calibrate, and optimize DW-MRI for single-subject biomarker development for Alzheimer’s Disease. We will integrate and optimize our strategies using large retrospective multi-site studies and validate the approaches on two distinct prospective cohorts. Specifically, we aim to: Aim 1: Optimize data-driven techniques for stability across sessions, scanners/sites, and field strengths Impact: Harmonized DW-MRI methods will increase sensitivity to Alzheimer’s Disease and its prodromal stages. Aim 2: Translate innovations in microstructural harmonization to structural connectivity (tractography) Impact: Harmonizing structural connectivity will improve understanding of white matter in Alzheimer’s Disease. Aim 3: Advance statistical tools for single-subject inference through normative database construction Impact: Data-driven resources for uncertainty estimation will enable robust single-single subject inference. Relevance and Impact on Healthcare: The proposed research will advance understanding of Alzheimer’s Disease through (1) quantitative harmonization of DW-MRI biomarkers, (2) protocols for harmonization of retrospective and prospective DW-MRI studies, and (3) new tools for single subject inference targeting older cohorts. We will organize workshops/challenges to maximize the translational impact on clinical science. The long-term goal of our research is to (1) provide a well-validated strategy to quantitatively evaluate DW-MRI data across sites, (2) enhance DW-MRI biomarkers for Alzheimer’s Disease, and (3) advance patient care. Our research strategy will transform the manner in which DW-MRI data are interpreted and enable single-subject machine learning to interpret brain properties. The resources, software, and visualization tools will be made freely available in open source through DIPY to facilitate continued innovation.