Influence of the normalization template on the outcome of statistical parametric mapping of PET scans

Abstract

Spatial normalization is an essential preprocessing step in statistical parametric mapping (SPM)-based analysis of PET scans. The standard template provided with the SPM99 software package was originally constructed using ¹⁵O-H₂O PET scans and is commonly applied regardless of the tracer actually used in the scans being analyzed. This work studies the effect of using three different normalization templates in the outcome of the statistical analysis of PET scans: (1) the standard SPM99 PET template; (2) an ¹⁸F-FDG PET template, constructed by averaging PET scans previously normalized to the standard template; and (3) an MRI-aided ¹⁸F-FDG PET template, constructed by averaging PET scans normalized according to the deformation parameters obtained from MRI scans. A strictly anatomical MRI normalization of each PET was used as a reference, under the rationale that a normalization based only upon MRI should provide higher spatial accuracy. The potential bias involved in the normalization process was estimated in a clinical SPM study comparing schizophrenic patients with control subjects. For each between-group comparison, three SPM maps were obtained, one for each template. To evaluate the influence of the template, these SPM maps were compared to the reference SPM map achieved using the anatomical normalization. SPMs obtained by MRI-aided normalization showed the highest spatial specificity, and also higher sensitivity when compared to the standard normalization using the SPM99 ¹⁵O-H₂O template. These results show that the use of the standard template under inappropriate conditions (different tracer or mental state) may lead to inconsistent interpretations of the statistical analysis.

Introduction

Statistical parametric mapping (SPM) is a method conceived to perform voxel-by-voxel statistical analysis of functional images (Friston et al., 1995). Spatial normalization is a required preprocessing step in intersubject statistical analysis that consists of applying the nonlinear deformations required to force every particular PET scan to match a reference template study. The algorithm minimizes the residual squared difference between the images being normalized and the template image (Ashburner and Friston, 1999). The main disadvantage of this approach lies in the total loss of natural or pathological variability in brain morphology. This problem might be of particular relevance when studying diseases like schizophrenia, known to involve changes in brain morphology (Lawrie and Abukmeil, 1998).

Normalization can be performed directly by deforming the PET scans until they match the PET template or indirectly by using an additional MRI template. In this latter case, the deformation parameters are determined from structural images from the same subjects and then applied to the PET scans. This MRI-aided spatial normalization is allegedly more accurate than the one performed by using only functional images, given the better anatomical information and higher spatial resolution of MRI images (Ashburner and Friston, 1999).

However, when MR images of the subjects under study are not available, it is only possible to perform a normalization based solely upon functional images. The SPM99 software package (Friston et al., 1999) includes a PET template that was originally created by averaging 12 ¹⁵O-H₂O-PET scans from normal subjects with eyes closed in resting condition. For the normalization to work properly, the contrast in the template and the patient images must be reasonably similar. In PET scans, the image contrast is determined by both the tracer and the mental state of the subject under study. Therefore, when the acquisition conditions between the template and the PET scans differ, the dissimilarity of the image contrast may affect the outcome of the normalization process. Although these different acquisition conditions may not compromise the convergence of the normalization, their effect on the final result of the statistical analysis is uncertain.

In the event of different acquisition conditions between the template and the actual PET scans, the construction of specific ad hoc templates has been proposed. One possible way of building a specific template consists of normalizing a set of ¹⁸F-FDG PET scans from a control group to the standard ¹⁵O-H₂O PET template, and then averaging and smoothing these normalized images (Signorini et al., 1999).

An alternative method to generate the template involves the use of anatomical MRI scans of the subjects in the control group. This method begins by co-registering the PET and MRI scans, then normalizing MRI scans to an MRI template, obtaining the deformation parameters that are finally applied to the control PET scans (Meyer et al., 1999). As in the previous method, these normalized scans are averaged and smoothed to generate the final template. In practice, however, most SPM analyses are performed by just using the standard template provided with the statistical software package SPM99, regardless of the tracer and the cerebral condition during the PET acquisition.

The impact of different registration algorithms on the statistical analysis of neuroimages has been previously studied. For instance, Freire and Mangin (2001) proved that motion correction algorithms may introduce spurious activations in fMRI studies using both real and simulated time series with artificial activations. Regarding PET, great attention has been paid to the evaluation of the anatomical precision of different spatial normalization algorithms Lancaster et al 1999, Sugiura et al 1999, Kochunov et al 2000, whereas the possible effect of using different templates on the final statistical result has not been investigated in depth.

Davatzikos et al. (2001) studied the effect of different normalization algorithms in the statistical outcome of SPM analysis by using PET phantom studies. They compared the methods available in SPM’95, SPM’96, and SPM99 with the STAR method (Davatzikos, 1997) that maps a parametric representation of the outer boundary of the brain and ventricles to the brain in the Talairach atlas (Talairach and Tournoux, 1988). Their results illustrate the importance of the normalization strategy, demonstrating that the use of anatomical MRI scans increases the sensitivity of the statistical results.

Ishii and colleagues (2001) examined the impact of two different brain normalization techniques: SPM99 and NEUROSTAT (Minoshima et al., 1994), on the metabolic patterns of Alzheimer’s disease patients as compared to healthy controls. Inconsistent results were found between these two normalization methods when applied to atrophied brains. In addition, they also showed that use of ¹⁸F-FDG and ¹⁵O-H₂O PET templates for normalizing ¹⁸F-FDG PET scans resulted in different extent and peak height of areas representing metabolic changes.

The aim of our study was to evaluate whether the choice of a particular normalization technique among those currently available in the literature could alter the clinical interpretation of SPM results in a realistic situation. Our setup is a case study comparing the statistical outcome of three different PET templates in an SPM analysis of schizophrenic patients and control subjects. Since we hypothesize that both the particular tracer used to construct the template and the potential anatomical alterations of the brain may bias the SPM analysis, three templates were chosen to encompass these factors: (1) the standard SPM99 PET template based on ¹⁵O-H₂O; (2) an ¹⁸F-FDG PET template constructed by averaging FDG-PET scans previously normalized to the standard template (Signorini et al., 1999); and (3) an MRI-¹⁸F-FDG PET template constructed by averaging PET scans normalized using T1-weighted MRI scans (Meyer et al., 1999). A reference statistical map was calculated by using a normalization procedure that estimates the deformation parameters from MRI images of each subject. This procedure yields a pure anatomical normalization in which the different brain anatomy of the subjects is put into correspondence, in opposition to the functional normalization achieved by using PET templates, where only brain functional data are registered across subjects.