PET scanners devoted to in vivo functional study have recently been developed, but autoradiography remains the reference technique for assessing cerebral glucose metabolism (CMRGlu) in rodents. Autoradiographs are conventionally subjected to region of interest (ROI) analysis, which is intrinsically hypothesis-driven and therefore not suitable for whole-brain investigation. Voxel-wise statistical methods of analysis have long been used to determine differences in brain activity during in vivo functional neuroimaging experiments. They have also recently been applied to 3D reconstructed autoradiographic volume images from rat brains. We present here a fully automated analysis for autoradiographic data combining (1) computerized procedures for the acquisition and 3D reconstruction of postmortem volume images and (2) spatial normalization followed by classical whole-brain voxel-wise statistical analysis. We also describe an additional procedure for characterizing functional differences between the right and left hemispheres of the brain. We compared two spatial normalization techniques and evaluated how the effect of choosing a particular normalization technique impacted on the statistical analysis. We also propose a small volume correction analysis to address the problem of multiple statistical comparisons. Lastly, we investigated the reliability of such analyses, by comparing their results qualitatively and quantitatively with those previously obtained with our semiautomated ROI-based analysis [Dubois, A., Dauguet, J., Herard, A.-S., Besret, L., Duchesnay, E., Frouin, V., Hantraye, P., Bonvento, G., Delzescaux, T., 2007. Automated three-dimensional analysis of histologic and autoradiographic rat brain sections: application to an activation study. J. Cereb. Blood Flow Metab. 27 (10), 1742-1755.]. Both voxel-wise statistical analyses led to the detection of consistent interhemispheric differences in CMRGlu. This work demonstrates the potential value and robustness of voxel-wise statistical methods for analyzing autoradiographic data sets.