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.

项目总结 阿尔茨海默氏症和相关痴呆症是一种日益严重的公共健康危机，影响着580万美国人，但 只有四种FDA批准的治疗阿尔茨海默氏症的药物，没有一种是治疗疾病的。 因此，早期发现和诊断是成功的患者管理的关键，需要生物标志物来 在临床试验中评估新疗法。脑白质改变与早期阿尔茨海默病的关系日益密切 疾病进展和弥散加权磁共振成像(DW-MRI)已包括在许多 全国性的研究。然而，由于缺乏一致性，DW-MRI数据的定量研究受到阻碍 采集协议、站点和扫描仪的变化。DW-MRI能够对大脑微结构进行量化 并且便于结构连通性映射。最近在校准方面取得了实质性进展，并 协调，以减少主体间的差异，并提高计算的衡量标准的可解释性。然而， 根本的挑战仍然是DW-MRI的临床应用(目前实施的)是 受到扫描仪间和站点间影响的困扰。 为了更好地理解阿尔茨海默病的结构变化，我们将构建和评估三个 单独的分析策略来表征、校准和优化单一对象生物标记物的DW-MRI 阿尔茨海默氏症的研究进展。我们将使用大型回顾来整合和优化我们的战略 对两个不同的预期队列进行多点研究并验证方法。具体来说，我们的目标是： 目标1：优化数据驱动的技术，以实现跨会话、扫描仪/站点和现场优势的稳定性 影响：协调的DW-MRI方法将提高对阿尔茨海默病及其前驱疾病阶段的敏感性。 目标2：将微观结构协调方面的创新转化为结构连通性(轨迹成像) 影响：协调结构连接将提高对阿尔茨海默病患者脑白质的理解。 目标3：通过建立标准化数据库，为单一主体推理提供先进的统计工具 影响：用于不确定性估计的数据驱动的资源将实现强大的单一主体推理。 相关性和对医疗保健的影响：拟议的研究将促进对阿尔茨海默氏症的理解 疾病通过(1)DW-MRI生物标记物的定量协调，(2)协调 回顾和前瞻性的DW-MRI研究，以及(3)针对老年人的单项受试者推断的新工具 一群人。我们将组织研讨会/挑战，以最大限度地提高翻译对临床科学的影响。这个 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 跨地点，(2)增强阿尔茨海默病的DW-MRI生物标记物，以及(3)促进患者护理。我们的 研究战略将改变解释DW-MRI数据的方式，并使单一受试者 机器学习来解释大脑的特性。将制作资源、软件和可视化工具 通过DIPY免费开放源码，以促进持续创新。

项目成果

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

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

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

Why rankings of biomedical image analysis competitions should be interpreted with care

• DOI: 10.1038/s41467-018-07619-7
• 发表时间: 2018-12-06
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Maier-Hein, Lena;Eisenmann, Matthias;Kopp-Schneider, Annette
• 通讯作者: Kopp-Schneider, Annette

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