This chapter discusses the transformations of the geometry, namely warping, that affect the shape and form of brain image data. The geometric transformations that are considered warps include global scaling, affine transformations, linear, nonlinear, and local deformations. Each of these transformations produces registered image sets that can be statistically and visually compared. One of the driving forces responsible for the importance of warping is the need to perform brain-to-brain comparisons. Warping strategies can be divided into intensity-driven or model-driven approaches, depending on the types of features that drive the mapping of one brain onto the other. The variety of warping approaches is equaled by their range of applications. There are classes of algorithms each suited to handling particular kinds of information, intensity patterns, or anatomical features. The product of warping algorithms not only provides the ability to make comparisons but also permits the generation of atlases and maps. The statistical product is often best accompanied by visualization. Incorporating interim displays and animating the spatial transformation have remarkable power for elucidating complex and significant morphological changes.

An Introduction to Brain Warping

A Population Based Multimodality Atlas of the Alzheimer's Brain: Integrating Pathology and Metabolism

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A Population Based Multimodality Atlas of the Alzheimer's Brain: Integrating Pathology and Metabolism