Wu Minjie, Carmichael Owen, Lopez-Garcia Pilar, Carter Cameron S, Aizenstein Howard J
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Hum Brain Mapp. 2006 Sep;27(9):747-54. doi: 10.1002/hbm.20216.
Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited-dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel-level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM.
功能图像分析中用于配准的典型软件包包括自动图像配准（AIR）和统计参数映射（SPM）。然而，这两种方法都具有有限维变形模型。一种完全可变形模型，它将用于粗略对齐的分段线性配准与用于体素级细化的恶魔算法相结合，允许更高程度的空间变形。这导致来自不同受试者的功能信号更准确地共定位，因此可以产生更可靠的组平均信号。我们通过一系列实验定量比较了三种不同配准方法的性能，发现完全可变形模型始终比AIR或SPM产生更准确的结构分割和更可靠的功能信号共定位。
