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.

项目概要和摘要 在阿尔茨海默病 (AD) 研究中，对人脑的纵向受试者内成像和分析给出了 我们对个体受试者早期疾病过程的时间动态有宝贵的见解，并允许 然而，纵向治疗成像工具尚未针对临床研究进行优化。 或用于非协调扫描的挑战包括减少串行磁共振的噪声。 成像 (MRI) 扫描，同时对每个时间点进行同等加权以避免偏差并适当考虑； 在图像强度、对比度、MR 失真和对象随时间运动变化的情况下，所有这些都会发生萎缩。 有许多通用工具可用于检测精心策划的研究数据的纵向变化（例如 ADNI） 其中扫描协议已在各个采集站点之间进行协调，以最大程度地减少差异失真 不幸的是，这些工具不能准确地工作。 不统一的 MRI 扫描包含大量可用的研究数据和临床数据，其中 主教在不同扫描仪上安排主题的实际需要导致扫描结果存在额外差异 因此，对于过去扫描或临床使用的回顾性分析至关重要。 开发成像工具，该工具对全局扫描仪引起的图像差异不可知，但对 微妙的神经解剖学变化，例如 AD 中的萎缩，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、实现和验证深度学习（DL）AD图像 用于检测由于存在大图像差异而导致的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 失真、扫描仪制造商以及图像中随时间变化的主体运动。 在一个受试者体内，存在一种与随时间推移获得的脑部扫描相关的物理变形，这与 专注于纵向的学科内研究使我们能够制作出超敏感的案例。 注册和变更检测工具的性能远远优于跨主题中使用的通用工具 研究，配准的目的只是为了找到我们的纵向的近似解剖学对应关系。 因此，成像框架能够学习将真正的神经解剖学变化与不相关的扭曲分开。 由于申请人具有计算背景，拟议的哈佛大学、麻省理工学院和麻省理工学院的培训计划 MGH 将在 K99 阶段重点关注神经科学和神经病学，以培养过渡所需的技能 申请人的目标是成为AD临床影像专家并推动R00阶段的独立。 突破目前 AD 研究的局限性，从根本上提高医疗保健质量。 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。