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... prior knowledge of the normal variability of brain size. The second step accounts for global nonlinear shape differences, which are modeled by a linear combination of smooth spatial basis functions (=-=Ashburner and Friston, 1999-=-). The nonlinear registration involves estimating the coefficients of the basis functions that minimize the residual squared difference between the image and the template, while simultaneously maximiz...

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...istering each of the images to the same template image, using the residual sum of squared differences as the matching criterion. The first step in spatial normalization involves estimating the optimum 12-parameter affine transformation to match images (Ashburner et al., 1997). A Bayesian framework is used, whereby the maximum a posteriori (MAP) estimate of the spatial transformation is made using prior knowledge of the normal variability in brain size. The second step accounts for global non-linear shape differences, which are modeled by a linear combination of smooth spatial basis functions (Ashburner and Friston, 1999). A masking procedure is used to weight the normalization to brain rather than nonbrain tissue. The spatially normalized images are resliced with a final voxel size of approximately 1.5 3 1.5 3 1.5 mm3. Segmentation. Scans were then segmented into grey matter, white matter, CSF, and other nonbrain partitions. SPM segmentation employs a mixture model cluster analysis to identify voxel intensities matching particular tissue types (grey matter, white matter and CSF) combined with an a priori knowledge of the spatial distribution of these tissues in normal subjects, derived from probability maps. ...

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... patient’s own scan is presented – so the readers can find relevant landmarks and unique features of the patient’s brain. Finally, contemporary normalization is fairly automated, so it is not as technically demanding, time consuming, and subjective as hand drawn lesions on standard templates. Normalization is a computationally intensive process and a great deal of research has gone into refining this technique. However, the basic concepts and terms are straightforward. In addition, modern implementations are fairly automatic and generally easy to use. The specific implementation used by SPM99 [1,8] will be described here (this software can be downloaded from www.fil.ion.ucl.ac.uk/spm/). There are other normalization packages that use similar intensity-based algorithms, such as AIR [20] and MNI AutoReg/ANIMAL [2]. It should be noted that there are techniques that use radically different algorithms [17] that are preferable in certain applications. However, SPM’s popularity with neuroimagers, robustness, and ability to normalize images with focal lesions (as discussed later) make it an appropriate example for a discussion of spatial normalization. Normalization is an iterative process for ...

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...ping using the correspondence function identified. Applications include intra-subject [12], inter-subject [13], [14], and inter-modality analysis [15], [16], [17], registration with annotated atlases =-=[18]-=-, [19], quantification and qualification of feature shapes and sizes [20], distortion compensation [21], [22] and motion detection [23], [24] and compensation [25]. Manuscript received September 6, 20...

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...es. Examples of such techniques include voxel-based morphometry (VBM), tensor based morphometry (TBM), deformation based morphometry (DBM), and hybrids of these methods (e.g., Ashburner et al., 1998; =-=Ashburner and Friston, 1999-=-; Ashburner and Friston, 2000; Davatzikos and Resnick, 1998; Christensen et al., 1997; Gaser et al., 1999). Alternative computational strategies can characterize gyral and sulcal pattern variations ac...

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...f smooth basis functions. The basis functions can have global support, i.e., spanning the entire image domain such as thin plate spline [73], polynomial [45], Gaussian [53] or trigonometric functions =-=[1]-=-, or local support, i.e., being nonzero in a subregion only, such as B-splines [96], [101] or localized radial basis functions [92]. When using basis functions such as splines, the number and distribu...