Retrospective Evaluation of PET-MRI Registration Algorithms

The purpose of this study is to evaluate the accuracy of registration positron emission tomography (PET) head images to the MRI-based brain atlas. The [¹⁸F]fluoro-2-deoxyglucose PET images were normalized to the MRI-based brain atlas using nine registration algorithms including objective functions of ratio image uniformity (RIU), normalized mutual information (NMI), and normalized cross correlation (CC) and transformation models of rigid-body, linear, affine, and nonlinear transformations. The accuracy of normalization was evaluated by visual inspection and quantified by the gray matter (GM) concordance between normalized PET images and the brain atlas. The linear and affine registration based on the RIU provided the best GM concordance (average similarity index of 0.71 for both). We also observed that the GM concordances of linear and affine registration were higher than those of the rigid and nonlinear registration among the methods evaluated.