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

项目总结和摘要 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力学有价值的见解，并允许 评估治疗效果。然而，纵向成像工具尚未针对临床研究进行优化 或者用于非协调扫描。挑战包括减少串联磁共振的噪声 成像（MRI）扫描，同时对每个时间点进行同等加权以避免偏倚;并适当考虑 萎缩，所有这些都存在随时间变化的图像强度、对比度、MR失真和受试者运动。 有许多通用工具可用于检测精心策划的研究数据中的纵向变化（如ADNI） 其中扫描协议已经在采集部位之间协调， 并且在数据发布之前去除梯度非线性。不幸的是，这些工具不能准确地工作， 不协调的MRI扫描，包括大部分可用的研究数据，以及临床数据，其中 临床医生在不同扫描仪上安排受检者的实际需要导致扫描中的额外差异 在多个扫描会话中获取。因此，对于过去扫描或临床使用的回顾性分析， 开发成像工具，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，如AD中的萎缩，这是早期疾病过程的高度预测。 为了解决上述问题，我们提出设计、实现和验证深度学习（DL）AD映像 用于在存在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 和B 0失真、扫描仪制造商和图像中的对象运动。我们利用这个事实 在受试者体内，存在一种物理变形，这种变形与跨时间获取的大脑扫描相关联，而不像 跨学科的案子专注于纵向受试者内研究使我们能够制作超灵敏的 注册和变化检测工具，大大优于通用的跨学科 研究，其中配准仅旨在找到近似的解剖学对应。我们的纵向 因此，成像框架能够学会将真实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，因此建议在哈佛、麻省理工学院和 MGH将在K99阶段专注于神经科学和神经病学，以培养过渡所需的技能 到R 00阶段的独立。申请人的目标是成为AD临床成像专家， 目前AD研究的局限性，从根本上提高医疗质量。我们 我相信，拟议的项目是朝着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和一般神经退行性疾病过程的临床成像和分析。

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.