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一种通过同时进行表面和体积流实现脑图谱配准的框架。

A framework for brain registration via simultaneous surface and volume flow.

作者信息

Joshi Anand, Leahy Richard, Toga Arthur W, Shattuck David

机构信息

Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA.

出版信息

Inf Process Med Imaging. 2009;21:576-88. doi: 10.1007/978-3-642-02498-6_48.

Abstract

Volumetric registration of brain MR images presents a challenging problem due to the wide variety of sulcal folding patterns. We present a novel volumetric registration method based on an intermediate parameter space in which the shape differences are normalized. First, we generate a 3D harmonic map of each brain volume to unit ball which is used as an intermediate space. Cortical surface features and volumetric intensity are then used to find a simultaneous surface and volume registration. We present a finite element method for the registration by using a tetrahedral volumetric mesh for registering the interior volumetric information and the corresponding triangulated mesh at the surface points. This framework aligns the convoluted sulcal folding patterns as well as the subcortical structures by allowing simultaneous flow of surface and volumes for registration. We describe the methodology and FEM implementation and then evaluate the method in terms of the overlap between segmented structures in coregistered brains.

摘要

由于脑沟折叠模式的多样性,脑磁共振图像的体积配准是一个具有挑战性的问题。我们提出了一种基于中间参数空间的新型体积配准方法,在该空间中形状差异被归一化。首先,我们生成每个脑体积到单位球的三维调和映射,用作中间空间。然后利用皮质表面特征和体积强度来实现表面和体积的同时配准。我们提出了一种有限元方法进行配准,使用四面体体积网格来配准内部体积信息,并在表面点使用相应的三角网格。该框架通过允许表面和体积同时流动进行配准,从而对齐复杂的脑沟折叠模式以及皮质下结构。我们描述了该方法和有限元实现,然后根据配准后大脑中分割结构之间的重叠来评估该方法。

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