Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
Marc Modat, Tom K. Vercauteren, Gerard R. Ridgway, David J. Hawkes, Nick C. Fox, Sébastien Ourselin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios.