TR&D3: Intrinsic Surface Mapping

项目总结- TR&D3：内在表面映射 对于脑成像研究，表面映射方法在各种科学研究中发挥了重要作用。 从跟踪青少年大脑的成熟到绘制大脑灰质萎缩模式， 阿尔茨海默病（AD）。然而，在当前的表面映射中存在两个基本限制 技术.首先，当前的方法通常在使用单位球之前用不同的大脑表面参数化。 他们的注册。在该参数化步骤期间不可避免的度量失真可能导致测量中的误差。 脑解剖结构的配准和疾病引起的变化的检测中的降低的功率。第二，当前 FreeSurfer等表面制图工具依赖于制图精度有限的几何要素 更高级的大脑区域，不考虑疾病相关的生物机制。在这个项目中，我们将 开发一个新的计算框架来克服这些基本限制。这种新颖的方法建立了 基于我们在解剖表面的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， 通过球面配准计算的地图中的误差。这一总体框架还有助于纳入 多模态成像特征以计算改进对准精度的多形态表面图 对应的解剖学和人脑的功能。总的来说，该项目有三个具体目标。目的 1.黎曼度量优化框架下曲面映射软件工具的开发。在 这一目标，我们将集中精力开发一个用户友好的软件工具集，实现算法， 在LB嵌入空间中的曲面上的黎曼度量优化（RMOS）。目标2.开发新型 RMOS表面映射方法由丰富的上下文特征驱动。为此，我们将开发一套丰富的 上下文特征，以驱动RMOS计算引擎，并提供更具解剖学意义的大脑 映射结果。目标3. Laplace-Beltrami纵向曲面映射方法的发展 嵌入空间在这个目标中，我们将使用RMOS框架来开发新的方法来研究 大脑解剖学的纵向进化在这个项目中开发的所有软件工具将持续分发 在我们LONIR网站上名为Metric Optimization for Computational Anatomy（莫卡）的软件中。

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.