Rigid Body Registration
Rigid Body Registration
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DOI：
10.1016/b978-012372560-8/50004-8
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发表时间：
2007-01-01
期刊：
影响因子：
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通讯作者：
Friston, K.
中科院分区：
文献类型：
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作者：
Ashburner, J.;Friston, K.
刚体配准是最简单的图像配准形式之一，因此本章提供了一个理想的框架，介绍一些概念，这些概念将用于后面描述的更复杂的配准方法。人脑的形状随着头部的移动变化很小，因此刚体变换可以用于对同一对象的不同头部位置进行建模。本章中描述的配准方法包括模态内或不同模态之间的配准，如正电子发射断层扫描（PET）。和磁共振成像（MRI）。两个图像的匹配是通过找到优化图像的一些相互功能的旋转和平移来执行的。模态内配准通常涉及通过最小化图像之间的均方差来匹配图像。对于模态间配准，匹配标准需要更复杂。图像配准在功能图像分析的许多方面都很重要。在成像神经科学中，特别是对于功能性MRI（fMRI），由于任何血液动力学响应而引起的信号变化与可能由受试者运动引起的明显信号差异相比可能很小。扫描仪中的受试者头部运动无法完全消除，因此作为预处理步骤执行回顾性运动校正。这对于受试者可以以与不同条件相关的方式在扫描仪中移动的实验尤其重要（Hajnal等人，1994年）。即使是微小的系统差异也可能导致大量扫描中积累的显著信号。如果没有适当的校正，与实验范式相关的受试者运动产生的伪影可能会出现激活。运动校正重要的第二个原因是它增加了灵敏度。t检验基于相对于残差方差的信号变化。残差方差由Karl Friston等人从统计参数映射的总和计算。
Rigid body registration is one of the simplest forms of image registration, so this chapter provides an ideal framework for introducing some of the concepts that will be used by the more complex registration methods described later. The shape of a human brain changes very little with head movement, so rigid body transformations can be used to model different head positions of the same subject. Registration methods described in this chapter include within modality, or between different modalities such as positron emission tomography (PET). and magnetic resonance imaging (MRI). Matching of two images is performed by finding the rotations and translations that optimize some mutual function of the images. Within-modality registration generally involves matching the images by minimizing the mean squared difference between them. For between-modality registration, the matching criterion needs to be more complex. Image registration is important in many aspects of functional image analysis. In imaging neuroscience, particularly for functional MRI (fMRI), the signal changes due to any haemodynamic response can be small compared to apparent signal differences that can result from subject movement. Subject head movement in the scanner cannot be completely eliminated, so retrospective motion correction is performed as a preprocessing step. This is especially important for experiments where subjects may move in the scanner in a way that is correlated with the different conditions (Hajnal et al., 1994). Even tiny systematic differences can result in a significant signal accumulating over numerous scans. Without suitable corrections, artefacts arising from subject movement correlated with the experimental paradigm may appear as activations. A second reason why motion correction is important is that it increases sensitivity. The t-test is based on the signal change relative to the residual variance. The residual variance is computed form the sum Statistical Parametric Mapping, by Karl Friston et al.