Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain.

中文翻译：

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图像配准在大脑绘图中的作用。


图像配准是各种生物医学成像应用中的关键步骤。它提供了将一个数据集与另一个数据集进行几何对齐的能力，并且是所有跨受试者、成像模式或跨时间比较数据集的成像应用程序的先决条件。配准算法还可以汇集和比较各个实验室的实验结果，构建基于人群的大脑图谱，以及创建检测结构和功能成像数据中的群体模式的系统。我们回顾了当今脑成像中使用的主要配准方法类型。我们关注它们的概念基础、底层数学以及它们在不同背景下的优点和缺点。我们描述了配准的主要目标，包括数据融合、变化量化、自动图像分割和标记、形状测量和病理检测。我们表明，配准算法与数字大脑图谱结合使用时具有巨大的潜力，数字大脑图谱充当参考系统，可以在其中比较大脑图像以进行统计分析。由此产生的配准方法是医学图像分析的基础，并且在大脑绘图背景下提供了一种阐明大脑解剖学或生理学的临床、人口统计或功能趋势的方法。