title: Longitudinal brain MRI analysis with uncertain registration.
abstract: In this paper we propose a novel approach for incorporating measures of spatial uncertainty, which are derived from non-rigid registration, into spatially normalised statistics. Current approaches to spatially normalised statistical analysis use point-estimates of the registration parameters. This is limiting as the registration will rarely be completely accurate, and therefore data smoothing is often used to compensate for the uncertainty of the mapping. We derive localised measurements of spatial uncertainty from a probabilistic registration framework, which provides a principled approach to image smoothing. We evaluate our method using longitudinal deformation features from a set of MR brain images acquired from the Alzheimer's Disease Neuroimaging Initiative. These images are spatially normalised using our probabilistic registration algorithm. The spatially normalised longitudinal features are adaptively smoothed according to the registration uncertainty. The proposed adaptive smoothing shows improved classification results, (84% correct Alzheimer's Disease vs. controls), over either not smoothing (79.6%), or using a Gaussian filter with sigma = 2mm (78.8%).
authors: Institute of Biomedical Engineering, University of Oxford, Oxford. ivor.simpson@eng.ox.ac.uk
authors: Ivor J A
authors: IJ
authors: Simpson
authors: Mark W
authors: MW
authors: Woolrich
authors: Adrian R
authors: AR
authors: Groves
authors: Julia A
authors: JA
authors: Schnabel
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016011:Normal Distribution; D011336:Probability; D015203:Reproducibility of Results; D012984:Software