title: Anatomy-preserving nonlinear registration of deep brain ROIs using confidence-based block-matching.
abstract: Brain atlases are commonly used in a number of applications such as MRI segmentation and surgery targetting. Our goal is to register a basal ganglia atlas to a subject using MR image registration. Existing registration methods are for the most part either too constrained (linear registration) or can deform deep brain ROIs into implausible anatomical shapes. We developed a block-matching registration method suitable for atlas registration, using a new confidence-based regularization of the vector field. The method was used to register a set of 17 manually segmented MRI onto one subject. Results show that basal ganglia structures were better registered than when using an affine registration method.
authors.affiliation: CNRS UPR640 - LENA, Paris, France. manik.bhattacharjee@chups.jussieu.fr
authors.forename: Manik
authors.initials: M
authors.lastname: Bhattacharjee
authors.forename: Alain
authors.initials: A
authors.lastname: Pitiot
authors.forename: Alexis
authors.initials: A
authors.lastname: Roche
authors.forename: Didier
authors.initials: D
authors.lastname: Dormont
authors.forename: Eric
authors.initials: E
authors.lastname: Bardinet
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique