title: Left-invariant metrics for diffeomorphic image registration with spatially-varying regularisation.
abstract: We present a new framework for diffeomorphic image registration which supports natural interpretations of spatially-varying metrics. This framework is based on left-invariant diffeomorphic metrics (LIDM) and is closely related to the now standard large deformation diffeomorphic metric mapping (LDDMM). We discuss the relationship between LIDM and LDDMM and introduce a computationally convenient class of spatially-varying metrics appropriate for both frameworks. Finally, we demonstrate the effectiveness of our method on a 2D toy example and on the 40 3D brain images of the LPBA40 dataset.
authors: Rotman Research Institute, Baycrest, Toronto, Canada.
authors: Tanya
authors: T
authors: Schmah
authors: CNRS, Institut de Mathématiques de Toulouse (UMR 5219), France.
authors: Laurent
authors: L
authors: Risser
authors: CEREMADE (UMR 7534), Université Paris Dauphine, France.
authors: François-Xavier
authors: FX
authors: Vialard
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; 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*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique