title: Analysis of serial magnetic resonance images of mouse brains using image registration.
abstract: The aim of this paper is to investigate techniques that can identify and quantify cross-sectional differences and longitudinal changes in vivo from magnetic resonance images of murine models of brain disease. Two different approaches have been compared. The first approach is a segmentation-based approach: Each subject at each time point is automatically segmented into a number of anatomical structures using atlas-based segmentation. This allows cross-sectional and longitudinal analyses of group differences on a structure-by-structure basis. The second approach is a deformation-based approach: Longitudinal changes are quantified by the registration of each subject's follow-up images to that subject's baseline image. In addition the baseline images can be registered to an atlas allowing voxel-wise analysis of cross-sectional differences between groups. Both approaches have been tested on two groups of mice: A transgenic model of Alzheimer's disease and a wild-type background strain, using serial imaging performed over the age range from 6-14 months. We show that both approaches are able to identify longitudinal and cross-sectional differences. However, atlas-based segmentation suffers from the inability to detect differences across populations and across time in regions which are much smaller than the anatomical regions. In contrast to this, the deformation-based approach can detect statistically significant differences in highly localized areas.
authors.affiliation: Department of Computing, South Kensington Campus, Imperial College, London, UK. address: satheesh@doc.ic.ac.uk
authors.forename: Satheesh
authors.initials: S
authors.lastname: Maheswaran
authors.forename: Hervé
authors.initials: H
authors.lastname: Barjat
authors.forename: Simon T
authors.initials: ST
authors.lastname: Bate
authors.forename: Paul
authors.initials: P
authors.lastname: Aljabar
authors.forename: Derek L G
authors.initials: DL
authors.lastname: Hill
authors.forename: Lorna
authors.initials: L
authors.lastname: Tilling
authors.forename: Neil
authors.initials: N
authors.lastname: Upton
authors.forename: Michael F
authors.initials: MF
authors.lastname: James
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique