12 March 2010 Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
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Abstract
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.
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Marc Modat, Marc Modat, Tom Vercauteren, Tom Vercauteren, Gerard R. Ridgway, Gerard R. Ridgway, David J. Hawkes, David J. Hawkes, Nick C. Fox, Nick C. Fox, Sébastien Ourselin, Sébastien Ourselin, } "Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76232K (12 March 2010); doi: 10.1117/12.843962; https://doi.org/10.1117/12.843962
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