Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease

阿尔茨海默病患者的大规模神经影像资料分析 摘要： 成像技术的进步为以多种方式研究阿尔茨海默病(AD)提供了极大的机会 这在以前是不可能的。这导致了各种大规模的成像研究，即ADNI，以发现 与广告相关的影像生物标志物。在这些成像研究中，图像配准在减少 混淆了受试者间的可变性，也增强了识别相关异常的统计能力 到公元后。然而，大规模成像数据的自动化处理，即涉及从数百到 数以千计的3D大脑图像并不是微不足道的，需要专门的计算工具。这个项目的目标是 就是开发一系列新颖的深度多层GroupWise配准方法，实现高效、高效和 同时配准可能存在较大解剖和外观差异的所有脑图像。另外， 为了适应从正在进行的大规模成像研究中获得的新图像，高效的 将进一步开发增量GroupWise注册方法，以避免耗费时间和资源 从头开始重新配准所有新的和现有的图像。 我们的关键思想是将复杂的GroupWise注册问题分解为小的- 易于解决的规模化注册任务，使大规模注册更易于管理 而且要快。具体来说，1)为了快速初始化大规模的GroupWise脑图像配准，我们将 在目标1中提出了一种基于随机森林的分层学习的地标检测算法 回归，以检测显著的解剖地标，然后将所有图像与检测到的地标联合对齐。 由于所有图像都分布在复杂的流形中，而且相似图像的配准也要快得多 更准确地说，我们建议首先建立一个图表，将每个图像只与相似的图像链接起来，然后 将GroupWise注册表示为动态图形收缩。这避免了将每个图像直接配准到 与传统方法一样，提高了计算速度和精度。2)至 显著加快并改进这种基于单层图形的GroupWise注册，我们将进一步 在目标2中开发了一种同时逐层图形构造的深层多层GroupWise配准 和分层配准。3)最后，显著提高注册的速度和准确性 从正在进行的大规模成像研究中获得的新图像，我们将在目标3中开发一种新的增量 一种重用已有图像先前配准结果指导配准的GroupWise配准方法 新的图像。具体地说，每个新图像都可以快速注册到现有的公共空间 通过寻找与其最相似的现有图像(S)。因此，所有新的和现有的图像将成为 在公共空间相似，然后可以快速更新为其整体GroupWise注册。 所有开发的计算工具将免费提供给研究社区，以加速 阿尔茨海默病的影像研究。

Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease Abstract: Advances in imaging technology offer great opportunities to study Alzheimer's disease (AD) in many ways that are not previously possible. This leads to various large-scale imaging studies, i.e., ADNI, for discovering AD-related imaging biomarkers. In these imaging studies, image registration plays a key role in reducing the confounding inter-subject variability and also enhancing the statistical power of identifying abnormalities related to AD. However, automated processing of large-scale imaging data, i.e., involving anything from hundreds to thousands of 3D brain images, is not trivial and requires dedicated computational tools. The goal of this project is to develop a series of novel deep multi-layer groupwise registration methods for effective, efficient and simultaneous registration of all brain images with possibly large anatomical and appearance differences. Also, to accommodate for new images acquired from the on-going large-scale imaging study, an efficient incremental groupwise registration method will be further developed to avoid time- and resource-consuming re-registration of all new and existing images from scratch. Our key idea is to break down the complex groupwise registration problem into hierarchical sets of small- scale registration tasks that can be solved easily, thus making the large-scale registration more manageable and fast. Specifically, 1) for fast initialization of large-scale groupwise registration of brain images, we will develop in Aim 1 a hierarchical learning-based landmark detection algorithm, based on random forest regression, to detect salient anatomical landmarks and then jointly align all images with detected landmarks. Since all images are distributed in a complex manifold and also the registration of similar images is much faster and more accurate, we propose to first build a graph to link each image only with similar images, and then formulate groupwise registration as dynamic graph shrinkage. This avoids direct registration of each image to the group-mean image as done in the conventional methods, thus improving both speed and accuracy. 2) To significantly speed up and also improve this single-layer graph-based groupwise registration, we will further develop in Aim 2 a deep multi-layer groupwise registration by simultaneous layer-by-layer graph construction and layer-wise registration. 3) Finally, to significantly increase both the speed and accuracy of registration for new images acquired from on-going large-scale imaging study, we will develop in Aim 3 a novel incremental groupwise registration method to reuse previous registration results of existing images for guiding registration of new images. Specifically, each new image can be quickly registered to the common space of existing images by finding its most similar existing image(s). Accordingly, all new and existing images will become similar in the common space and then can be quickly updated for their overall groupwise registration. All computational tools developed will be made freely available to the research community, for accelerating the imaging study of Alzheimer's disease.