Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images.
Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images.
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DOI：
10.1016/j.neuroimage.2011.01.067
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发表时间：
2011-05-01
期刊：
影响因子：
5.7
通讯作者：
Qiu, Anqi
中科院分区：
文献类型：
--
作者：
Du, Jia;Younes, Laurent;Qiu, Anqi
本文介绍了一种新的大变形的全脑注册沟回曲线，皮层表面，并同时进行强度的图像，从一个主题到另一个通过流的同态度量映射算法。据我们所知，这是第一次从一个大脑到另一个大脑的同构度量以统一的方式在强度图像和点集（如曲线和曲面）的形状空间中导出。我们描述的欧拉-拉格朗日方程与此算法的动量，线性变换的速度矢量场的非纯流。通过引入一类计算友好的内核，解决这个变分问题，其中涉及大规模的内核卷积在一个不规则的网格，是可行的数值实现。我们应用该算法来对齐磁共振脑数据。我们的全脑映射结果表明，我们的算法优于基于图像的LDDMM算法的映射精度的脑回/脑沟曲线，脑沟区域，皮层和皮层下的分割。此外，我们的算法提供了更好的全脑对齐相结合的体积和表面注册和层次属性匹配机制的弹性注册（HAMMER）在皮层和皮层下体积分割。
This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration and hierarchical attribute matching mechanism for elastic registration (HAMMER) in terms of cortical and subcortical volume segmentation.
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影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
DOI：
10.1007/11566489_43
发表时间：
2005-01-01
期刊：
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2005, PT 2
影响因子：
--
作者：
Clouchoux, C;Coulon, O;Régis, J
通讯作者：
Régis, J
影响因子：
10.6
作者：
Christensen, GE;Joshi, SC;Miller, MI
通讯作者：
Miller, MI
影响因子：
4.5
作者：
Davatzikos, C
通讯作者：
Davatzikos, C