Volumetric transformation of brain anatomy
Volumetric transformation of brain anatomy
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DOI：
10.1109/42.650882
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发表时间：
1997-12-01
影响因子：
10.6
通讯作者：
Miller, MI
中科院分区：
文献类型：
--
作者：
Christensen, GE;Joshi, SC;Miller, MI
本文介绍了猕猴枕叶和全脑冷冻切片图像以及通过磁共振成像成像的人脑深部脑结构的三维（3-D）解剖图像数据的同构变换，这些变换以分层方式生成，适应全局和局部解剖细节，初始低维配准是通过将变换约束在低维基础上来完成的，所述基础由放置在解剖结构中的预定位置处的弹性算子的绿色函数和弹性算子的本征函数定义，高维大变形是通过模板和目标图像体积之间的不匹配产生的矢量场，该不匹配被约束为Navier-Stokes流体模型的解。作为该过程的一部分，跟踪变换的雅可比矩阵，以确保生成非同态。它示出的二次正则化方法，如拉普拉斯，双调和，和线性弹性模型的约束下的变换，不确保变换保持拓扑结构，因此，必须只用于粗全球注册。
This paper presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery, These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail, The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis, The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator, The high-dimensional large deformations are vector fields generated via the mismatch between the template and target-image volumes constrained to be the solution of a Navier-Stokes fluid model. As part of this procedure, the Jacobian of the transformation is tracked, insuring the generation of diffeomorphisms. It is shown that transformations constrained by quadratic regularization methods such as the Laplacian, biharmonic, and linear elasticity models, do not ensure that the transformation maintains topology and, therefore, must only be used for coarse global registration.