Ultra-precision clinical imaging and detection of Alzheimers Disease using deep learning

使用深度学习进行超精密临床成像和阿尔茨海默病检测

• 批准号: 10643456
• 负责人: Sean Isaac Young
• 金额: $ 13.39万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643456
• 关键词: Accounting; Address; Aging; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Anatomy; Atrophic; Award; Biological Models; Brain; Brain imaging; Brain scan; Clinical; Clinical Data; Clinical Research; Clinical Trials; Communities; Compensation; Computer software; Consumption; Data; Data Set; Detection; Disease; Disease model; Early Diagnosis; Early Intervention; Ensure; Exposure to; Functional disorder; Goals; Human; Image; Image Analysis; Imaging Device; Individual; Learning; Longitudinal cohort; MRI Scans; Magnetic Resonance Imaging; Manufacturer; Masks; Massachusetts; Methods; Modality; Motion; Neuroanatomy; Neurodegenerative Disorders; Neurology; Neurosciences; Noise; Phase; Physics; Process; Protocols documentation; Regional Anatomy; Relaxation; Research; Scanning; Schedule; Sensitivity and Specificity; Serial Magnetic Resonance Imaging; Site; Structure; Study Subject; Supervision; Surrogate Endpoint; Time; Training; Training Programs; Treatment Efficacy; clinical imaging; computerized tools; contrast imaging; cranium; deep learning; deep neural network; denoising; design; health care quality; image processing; image registration; imaging Segmentation; imaging modality; improved; insight; interest; longitudinal analysis; morphometry; nervous system disorder; neural network; neuroimaging; serial imaging; simulation; skills; spatiotemporal; supervised learning; tool

PROJECT SUMMARY AND ABSTRACT In Alzheimer’s Disease (AD) studies, longitudinal within-subject imaging and analysis of the human brain gives us valuable insight into the temporal dynamics of the early disease process in individual subjects and allows to assess therapeutic efficacy. However, longitudinal imaging tools have not yet been optimized for clinical studies or for use on nonharmonized scans. Challenges include reduction of noise across serial magnetic resonance imaging (MRI) scans while weighting each time point equally to avoid biases; and appropriately accounting for atrophy all in the presence of varying image intensity, contrasts, MR distortions and subject motion across time. Many general tools exist for detecting longitudinal change in carefully curated research data (such as ADNI) in which the scan protocol has been harmonized across acquisition sites so as to minimize differential distortion and gradient nonlinearities removed prior to data release. Unfortunately, these tools do not work accurately for unharmonized MRI scans that comprise the bulk of the research data available, and on clinical data, where the practical need for clinicians to schedule a subject on different scanners leads to additional differences in scans acquired across multiple scan sessions. For retrospective analysis of past scans or clinical use, it is thus critical to develop imaging tools that are agnostic to global scanner-induced differences in images but very sensitive to subtle neuroanatomical change, such as atrophy in AD, that is highly predictive of the early disease process. To address the above issues, we propose to design, implement and validate a deep learning (DL) AD image analysis framework for detecting neuroanatomical change in the presence of large image differences due to the acquisition process itself, including the field strength, receive coil, sequence parameters, gradient nonlinearities and B0 distortions, scanner manufacturer, and subject motion in the images across time. We leverage the fact that, within a subject, there is a physical deformation that relates the brain scans acquired across time unlike the cross-subject case. Focusing exclusively on longitudinal within-subject studies allows us to craft ultra-sensitive registration and change detection tools that drastically outperform general purpose ones used in cross-subject studies, where registration is intended only to find approximate anatomical correspondences. Our longitudinal imaging framework is thus able to learn to disentangle true neuroanatomical change from irrelevant distortions. Since the applicant has a computational background, the proposed training program at Harvard, MIT and MGH will focus on neuroscience and neurology during the K99 phase to develop the skills needed to transition to independence in the R00 phase. The applicant aims to become an expert in clinical imaging of AD and push the limits of what is currently possible in AD research, fundamentally enhancing the quality of healthcare. We believe that the proposed project is a first step in this direction and the tools developed will further pave the way for clinical imaging and analysis of AD and neurodegenerative disease processes in general.

项目摘要和摘要 在阿尔茨海默氏病（AD）研究中，人脑的纵向内部成像和分析给出了 美国重视对各个受试者早期疾病过程暂时动态的洞察力，并允许 评估治疗效率。但是，纵向成像工具尚未针对临床研究进行优化 或用于非锤子扫描。挑战包括跨串行磁共振的噪声降低 成像（MRI）在每个时间点加权时进行扫描以避免偏见；并适当地考虑 萎缩均在不同的图像强度，对比度，MR畸变和受试者运动的情况下，萎缩。 存在许多通用工具，用于检测精心策划的研究数据中的纵向变化（例如ADNI） 其中扫描协议已在收购位点进行协调以最大程度地减少差异失真 在数据释放之前删除了梯度非线性。不幸的是，这些工具不能准确地适用于 不戒备的MRI扫描包括大部分可用的研究数据，以及临床数据 临床医生以不同的扫描仪安排主题的实际需求会导致扫描的其他差异 在多次扫描课程中获得。为了回顾过去的扫描或临床使用，这是至关重要的 开发对全球扫描仪引起的图像差异不可知的成像工具，但对 微妙的神经解剖学变化，例如AD的萎缩，可以高度预测早期疾病过程。 为了解决上述问题，我们建议设计，实施和验证深度学习（DL）广告图像 在存在较大图像差异的情况下，用于检测神经解剖学变化的分析框架 采集过程本身，包括场强，接收线圈，序列参数，梯度非线性 和B0扭曲，扫描仪制造商以及图像中的主题运动。我们利用事实 在一个受试者中，有一个物理变形，与随着时间的时间获取的大脑扫描有关 交叉对象。专注于纵向内部研究，使我们能够制造超敏感的 注册和更改检测工具极大地超过了跨主题中使用的检测工具 研究，注册仅旨在找到近似的解剖学对应。我们的纵向 因此，成像框架能够学会从无关的扭曲中解除真正的神经解剖学变化。 由于申请人具有计算背景，因此在哈佛大学，麻省理工学院和 MGH将重点关注K99阶段的神经科学和神经病学，以发展过渡所需的技能 在R00阶段独立。申请人的目标是成为广告临床成像的专家 当前广告研究中可能发生的局限性从根本上提高了医疗保健的质量。我们 相信拟议的项目是朝这个方向迈出的第一步，开发的工具将进一步铺平道路 用于AD和神经退行性疾病过程的临床成像和分析。