Longitudinal registration has been used to map brain atrophy and tissue loss patterns over time, in both healthy and demented subjects. However, we have not seen a thorough application of the geodesic shooting in diffeomorphisms framework for this task. The registration model is complex and several choices must be made that may significantly impact the quality of results. One of these decisions is which image matching functional should drive the registration. We investigate four matching functionals for atrophy quantification using geodesic shooting in diffeomorphisms. We check if the choice of matching functional has an impact on the correlation of atrophy scores with clinical variables. We also check the impact of matching functional choice on estimates of the N80 sample size for hypothetical clinical trials that test for slowing of brain atrophy. We find that the mutual information function, which has primarily been used for linear and multi-modal registration, achieves comparable correlation with clinical variables to other matching functionals while yielding better sample size estimates.
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