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 在阿尔茨海默病(AD)研究中，对人脑的纵向内部成像和分析给出了 US对个体受试者早期疾病过程的时间动态有价值的洞察，并允许 评估治疗效果。然而，纵向成像工具尚未优化用于临床研究。 或用于非协调扫描。挑战包括减少序列磁共振中的噪声 成像(MRI)扫描时对每个时间点进行平均加权，以避免偏差；并适当地说明 在不同的图像强度、对比度、MR扭曲和受试者随时间运动的情况下都会出现萎缩。 有许多通用工具可用于检测精心管理的研究数据(如ADNI)中的纵向变化 其中扫描协议已经跨采集点进行了协调，以便最大限度地减少差分失真 以及在数据发布之前去除的梯度非线性。不幸的是，这些工具不能准确地用于 未协调的MRI扫描，构成了大部分可用研究数据，以及临床数据，其中 临床医生在不同扫描仪上安排受试者的实际需要导致了扫描的额外差异 跨多个扫描会话获取。因此，对于过去的扫描或临床使用的回顾分析，它是至关重要的 开发不受全局扫描仪引起的图像差异影响但非常敏感的成像工具 微妙的神经解剖学变化，如AD的萎缩，对早期疾病过程具有很高的预测性。 为了解决上述问题，我们建议设计、实现和验证深度学习(DL)AD镜像 在存在较大图像差异的情况下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 以及B0失真、扫描仪制造商和图像中的对象随时间运动。我们利用这一事实 在受试者体内，有一种物理变形，它与随时间获得的大脑扫描有关，而不是 跨主体案件。专注于纵向的受试者内部研究使我们能够制作超敏感的 注册和更改检测工具的性能大大优于在跨主题中使用的通用工具 研究，注册的目的只是为了找到大致的解剖学对应。我们的纵向 因此，成像框架能够学习将真实的神经解剖变化从无关的扭曲中分离出来。 由于申请者有计算背景，哈佛、麻省理工学院和 MGH将在K99阶段专注于神经科学和神经学，以发展过渡所需的技能 在R00阶段走向独立。申请者的目标是成为AD和PUSH的临床影像专家 限制目前AD研究的可能性，从根本上提高医疗保健的质量。我们 相信拟议的项目是朝着这个方向迈出的第一步，所开发的工具将进一步铺平道路 用于临床成像和分析AD和神经退行性疾病的一般过程。