We have described a model-based approach to evaluating the results of NRR of a group of images. The most important advantage of the new method is that it does not require any ground truth, but depends solely on the registered images themselves.
We have validated the approach by studying the effect of perturbing, progressively, the registration of an initially registered set of images, comparing the results with those obtained using a 'gold standard' measure based on the overlap of ground-truth anatomical labels. We have shown that our new method provides measures of registration accuracy that are monotonic functions of the known misregistration, and that one, Specificity, provides a more sensitive measure of misregistration than the approach based on ground truth.
The model-based approach requires a distance measure in image space, and we have also demonstrated that the use of shuffle distance, rather than Euclidean distance, improves the sensitivity of the approach.
We have further validated the approach, and illustrated its application, by performing a comparative evaluation of the results obtained using three different NRR algorithms, demonstrating the superiority of a fully-groupwise algorithm over a repeated pairwise approach.
It is important to emphasise that our approach is not restricted to evaluating model-based NRR algorithms, though we presented results for one such method; the model-based measures of registration accuracy can be applied to any set of non-rigidly registered images, however they were obtained. We have discussed the possibility of a bias in favour of model-based methods of registration and conclude that there is no major problem, though it would be desirable to compare results obtained using a range of ground-truth-free methods of evaluation.