Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI

控制质量并捕捉高级扩散加权 MRI 的不确定性

• 批准号: 10683306
• 负责人: Bennett A. Landman
• 金额: $ 63.25万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-20 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683306
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; American; Anatomy; Architecture; Biological Markers; Blood Vessels; Brain; Brain Mapping; Calibration; Cerebrum; Chemicals; Clinical Research; Clinical Sciences; Clinical Trials; Cognitive; Coupled; Data; Data Analyses; Data Set; Databases; Detection; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Dissection; Early Diagnosis; Early Intervention; Educational workshop; Ensure; Evaluation; FDA approved; Functional disorder; Goals; Healthcare; Histology; Image; Individual; Intervention; Investigation; Machine Learning; Maps; Measures; Modality; Modeling; Morphologic artifacts; Neurons; Noise; Normal Range; Patient Care; Patients; Pharmaceutical Preparations; Physiological; Prognosis; Property; Prospective cohort; Protocols documentation; Public Health; Quality Control; Research; Resources; Role; Scanning; Sensitivity and Specificity; Severity of illness; Site; Software Tools; Specificity; Structure; System; Techniques; Tissue Model; Tissues; Translating; Uncertainty; Variant; Visualization software; aging brain; biomarker development; clinical application; cohort; data integration; deep neural network; disease prognostic; effective intervention; imaging modality; imaging study; improved; in vivo; innovation; magnetic resonance imaging biomarker; neuroimaging; novel therapeutics; open source; prospective; success; tool; tractography; translational impact; virtual; virtual biopsy; white matter; white matter change

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.

项目摘要 阿尔茨海默氏病和相关痴呆症是一场影响580万美国人的公共卫生危机，但 阿尔茨海默氏病只有四种FDA批准的药物，它们都没有疾病改良。 因此，早期发现和诊断是成功的患者管理的关键，并且需要生物标记物 评估临床试验中的新疗法。白质变化越来越涉及阿尔茨海默氏症早期 疾病进展和扩散加权磁共振成像（DW-MRI）已包括在许多 国家规模研究。但是，由于缺乏一致性，DW-MRI数据的定量投资受到阻碍 进行采集协议，站点和扫描仪的变化。 DW-MRI可以量化脑微观结构 并促进结构连通映射。最近的校准和 协调以减少受试者间方差并提高计算措施的解释性。但是， 基本挑战仍然是DW-MRI的临床应用（当前实施）是 被扫描仪和现场效应混淆。 为了提高对阿尔茨海默氏病结构变化的理解，我们将构建和评估三个 单独的分析策略以表征，校准和优化单人物生物标志物的DW-MRI 阿尔茨海默氏病的发展。我们将使用大型回顾性整合和优化我们的策略 多站点研究并验证了两个不同的前瞻性队列方法。具体来说，我们的目标是： AIM 1：优化跨会话，扫描仪/站点和现场优势的数据驱动技术 影响：统一的DW-MRI方法将增加对阿尔茨海默氏病及其前驱阶段的敏感性。 目标2：将微观结构的创新转化为结构连通性（Tractography） 影响：协调结构连通性将改善对阿尔茨海默氏病白质的理解。 AIM 3：通过普通数据库构建的单个受试者推断的提前统计工具 影响：数据驱动的不确定性估计资源将实现鲁棒的单个单个主题推断。 相关性和对医疗保健的影响：拟议的研究将提高对阿尔茨海默氏症的理解 通过（1）DW-MRI生物标志物的定量统一，（2）协调的方案 回顾性和前瞻性DW-MRI研究，以及（3）针对较老的单一主题推理的新工具 同伙。我们将组织研讨会/挑战，以最大程度地提高对临床科学的翻译影响。这 我们研究的长期目标是（1）提供验证良好的策略，以定量评估DW-MRI数据 在各个地点，（2）增强了阿尔茨海默氏病的DW-MRI生物标志物，以及（3）预先患者护理。我们的 研究策略将改变解释DW-MRI数据的方式并启用单个主体 机器学习解释大脑特性。将制作资源，软件和可视化工具 通过DIPY在开源中免费提供，以促进持续创新。

项目成果

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators.

通过开发深度学习稳定微架构估计器来协调 1.5T/3T 扩散加权 MRI。

• DOI: 10.1117/12.2512902
• 发表时间: 2019
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Nath,Vishwesh;Remedios,Samuel;Parvathaneni,Prasanna;Hansen,ColinB;Bayrak,RozaG;Bermudez,Camilo;Blaber,JustinA;Schilling,KurtG;Janve,VaibhavA;Gao,Yurui;Huo,Yuankai;Lyu,Ilwoo;Williams,Owen;Resnick,Susan;Beason-Held,Lori;Ro
• 通讯作者: Ro

Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols.

跨临床磁共振成像协议的 SLANT 全脑分割的再现性评估。

• DOI: 10.1117/12.2512561
• 发表时间: 2019
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Xiong,Yunxi;Huo,Yuankai;Wang,Jiachen;Davis,LTaylor;McHugo,Maureen;Landman,BennettA
• 通讯作者: Landman,BennettA

Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers.

• DOI: 10.1007/978-3-030-60548-3_17
• 发表时间: 2020-10
• 期刊: Lecture notes-monograph series
• 作者: Remedios SW;Butman JA;Landman BA;Pham DL
• 通讯作者: Pham DL

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury.

分布式深度学习，用于脑外伤后 CT 成像的稳健多部位分割。

• DOI: 10.1117/12.2511997
• 发表时间: 2019
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Remedios,Samuel;Roy,Snehashis;Blaber,Justin;Bermudez,Camilo;Nath,Vishwesh;Patel,MayurB;Butman,JohnA;Landman,BennettA;Pham,DzungL
• 通讯作者: Pham,DzungL

Communication-efficient federated learning for multi-institutional medical image classification

• DOI: 10.1117/12.2611654
• 发表时间: 2022-04
• 作者: Shuang Zhou;B. Landman;Yuankai Huo;A. Gokhale