In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking-masking the areas used in the calculation of image difference-to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions.
Copyright 2001 Academic Press.