Automated method for small-animal PET image registration with intrinsic validation

Purpose: We propose and compare different registration approaches to align small-animal PET studies and a procedure to validate the results by means of objective registration consistency measurements. Procedures: We have applied a registration algorithm based on information theory, using different approaches to mask the reference image. The registration consistency allows for the detection of incorrect registrations. This methodology has been evaluated on a test dataset (FDG-PET rat brain images). Results: The results show that a multiresolution two-step registration approach based on the use of the whole image at the low resolution step, while masking the brain at the high resolution step, provides the best robustness (87.5% registration success) and highest accuracy (0.67-mm average). Conclusions: The major advantages of our approach are minimal user interaction and automatic assessment of the registration error, avoiding visual inspection of the results, thus facilitating the accurate, objective, and rapid analysis of large groups of rodent PET images.