  Probabilistic image registration methods estimate the posterior distribution
of transformation. The conventional way of interpreting the transformation
posterior is to use the mode as the most likely transformation and assign its
corresponding intensity to the registered voxel. Meanwhile, summary statistics
of the posterior are employed to evaluate the registration uncertainty, that is
the trustworthiness of the registered image. Despite the wide acceptance, this
convention has never been justified. In this paper, based on illustrative
examples, we question the correctness and usefulness of conventional methods.
In order to faithfully translate the transformation posterior, we propose to
encode the variability of values into a novel data type called ensemble fields.
Ensemble fields can serve as a complement to the registered image and a
foundation for developing advanced methods to characterize the uncertainty in
registration-based tasks. We demonstrate the potential of ensemble fields by
pilot examples
