Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI

项目摘要 阿尔茨海默病和相关的痴呆症是一个日益严重的公共卫生危机，影响着580万美国人， 只有四种FDA批准的治疗阿尔茨海默病的药物，没有一种是改善疾病的。 因此，早期检测和诊断是成功的患者管理的关键，并且需要生物标志物用于 在临床试验中评估新疗法。白色物质变化越来越多地与早期阿尔茨海默氏症有关 疾病进展和弥散加权磁共振成像（DW-MRI）已被纳入许多 全国范围的研究。然而，DW-MRI数据的定量研究由于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能够量化大脑微观结构 并且便于结构连接映射。最近在校准方面取得了重大进展， 协调，以减少受试者之间的差异，提高计算测量的可解释性。然而 根本的挑战仍然是DW-MRI的临床应用（如目前实施的）， 受到扫描仪间和部位间效应的混淆。 为了提高对阿尔茨海默病结构变化的理解，我们将构建和评估三个 单独的分析策略，用于表征、校准和优化单个受试者生物标志物的DW-MRI 阿尔茨海默病的发展。我们将使用大型回顾性分析工具整合和优化我们的战略 多中心研究，并验证了两个不同的前瞻性队列的方法。具体而言，我们的目标是： 目标1：优化数据驱动技术，实现跨会话、扫描仪/站点和场强的稳定性 影响：协调DW-MRI方法将增加对阿尔茨海默病及其前驱期的敏感性。 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中白色物质的理解。 目标3：通过规范数据库建设，推进单学科推理的统计工具 影响：用于不确定性估计的数据驱动资源将实现强大的单-单主题推理。 对医疗保健的相关性和影响：拟议的研究将促进对阿尔茨海默氏症的理解 通过（1）DW-MRI生物标志物的定量协调，（2） 回顾性和前瞻性DW-MRI研究，以及（3）针对老年人的单一受试者推断的新工具 同伙我们将组织研讨会/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）提供一个有效的策略来定量评估DW-MRI数据 （2）增强阿尔茨海默病的DW-MRI生物标志物，（3）推进患者护理。我们 研究策略将改变DW-MRI数据的解释方式， 机器学习来解释大脑的特性。资源、软件和可视化工具将由 通过DIPY以开源形式免费提供，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.