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This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised 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 traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation.

Original publication

DOI

10.1016/j.media.2015.08.006

Type

Journal article

Journal

Med Image Anal

Publication Date

12/2015

Volume

26

Pages

203 - 216

Keywords

Bayesian inference, Medical image registration, Registration uncertainty, Regularisation, Algorithms, Bayes Theorem, Brain, Computer Simulation, Data Interpretation, Statistical, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Statistical, Nonlinear Dynamics, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Spatio-Temporal Analysis, Subtraction Technique