title: A Bayesian approach for spatially adaptive regularisation in non-rigid registration.
abstract: This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences.
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: Ivor J A
authors.initials: IJ
authors.lastname: Simpson
authors.affiliation: Oxford Centre for Human Brain Activity, University of Oxford.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Woolrich
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: Manuel Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: David M
authors.initials: DM
authors.lastname: Cash
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: Marc
authors.initials: M
authors.lastname: Modat
authors.affiliation: Institute of Biomedical Engineering, University of Oxford.
authors.forename: Julia A
authors.initials: JA
authors.lastname: Schnabel
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique