TR&D3: Intrinsic Surface Mapping

项目总结--tr&d3：本征表面测绘 在脑成像研究中，表面映射方法在各种科学研究中发挥了重要作用 从跟踪青春期大脑成熟到绘制灰质萎缩模式的发现 阿尔茨海默病(AD)。但是，当前的表面制图存在两个基本限制 技巧。首先，目前的方法通常是使用单位球体来对不同的大脑表面进行参数化 他们的登记。在此参数化步骤中不可避免的度量扭曲可能会导致 记录大脑解剖结构和降低检测疾病引起的变化的能力。第二，当前 表面制图工具(如freesurfer)依赖于制图精度有限的几何要素 高阶大脑区域，不考虑与疾病相关的生物学机制。在这个项目中，我们将 开发一种新的计算框架来克服这些基本限制。这种新的方法建立了 基于我们在解剖曲面的Laplace-Beltrami嵌入空间中的一系列形状分析工作。这 嵌入是等距的，因此它消除了由于球面参数化而导致的度量失真 球面配准计算的地图误差。这一一般框架还使纳入 用于计算可提高配准精度的微分同胚面图的多模式成像功能 人脑的相应解剖结构和功能。总体而言，这个项目有三个具体目标。目标 1.开发了黎曼度量优化框架下的表面绘图软件工具。在……里面 为此，我们将专注于开发一个用户友好的软件工具集，它实现了以下算法 LB嵌入空间中曲面上的黎曼度量优化(RMOS)目标2.小说的发展 由丰富的上下文功能驱动的RMOS曲面映射方法。为了实现这一目标，我们将开发一套丰富的 背景功能驱动RMOS计算引擎并提供更具解剖学意义的大脑 映射结果。目的3.拉普拉斯-贝尔特拉米纵向表面测绘方法的发展 嵌入空间。为此，我们将利用RMOS框架开发新的方法来研究 大脑解剖学的纵向演变。在这个项目中开发的所有软件工具都将继续分发 在我们的软件名为度量优化计算解剖学(MOCA)在LONIR网站上。

PROJECT SUMMARY - TR&D3: INTRINSIC SURFACE MAPPING For brain imaging studies, surface mapping methods have played an important role in various scientific discoveries from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease (AD). There are, however, two fundamental limitations in current surface mapping techniques. Firstly, current methods typically parameterize different brain surfaces with the unit sphere before their registration. The inevitable metric distortions during this parameterization step can lead to errors in the registration of brain anatomy and reduced power in the detection of disease induced changes. Secondly, current surface mapping tools such as FreeSurfer depend on geometric features that have limited accuracy in mapping high order brain regions and do not consider disease-related biological mechanisms. In this project, we will develop a novel computational framework to overcome these fundamental limitations. This novel approach builds upon our series of shape analysis work in the Laplace-Beltrami embedding space of anatomical surfaces. This embedding is isometric, so it eliminates the metric distortion due to spherical parameterization and resulting errors in the maps computed by spherical registration. This general framework also enables the incorporation of multimodal imaging features to compute diffeomorphic surface maps that improve the accuracy in aligning corresponding anatomy and functions of human brains. Overall there are three specific aims in this project. Aim 1. Development of the surface mapping software tools under the Riemannian metric optimization framework. In this aim, we will focus on developing a user friendly software toolset that implements the algorithms for Riemannian Metric Optimization on Surfaces (RMOS) in the LB embedding space. Aim 2. Development of novel RMOS surface mapping methods driven by rich contextual features. In this aim, we will develop a rich set of contextual features to drive the RMOS computational engine and provide more anatomically meaningful brain mapping results. Aim 3. Development of longitudinal surface mapping methods in the Laplace-Beltrami embedding space. In this aim, we will use the RMOS framework to develop novel methods for studying the longitudinal evolution of brain anatomy. All software tools developed in this project will be continuously distributed in our software called Metric Optimization for Computational Anatomy (MOCA) on LONIR website.