Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach

Brain surface registration is an important tool for characterizing cortical anatomical variations and understanding their roles in normal cortical development and psychiatric diseases. However, surface registration remains challenging due to complicated cortical anatomy and its large differences acr...

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Bibliographic Details
Published in:IEEE transactions on image processing 2016-09, Vol.25 (9), p.4061-4074
Main Authors: Tan, Mingzhen, Qiu, Anqi
Format: Article
Language:English
Subjects:
Algorithms
Brain
Bundling
Coarsening
Computational efficiency
Construction
cortical surface registration
Deformation
diffeomorphism
Diseases
Image resolution
Kernel
large deformation
Mapping
Measurement
Multiresolution analysis
Parametrization
Registration
Surface morphology
Surface treatment
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Summary:Brain surface registration is an important tool for characterizing cortical anatomical variations and understanding their roles in normal cortical development and psychiatric diseases. However, surface registration remains challenging due to complicated cortical anatomy and its large differences across individuals. In this paper, we propose a fast coarse-to-fine algorithm for surface registration by adapting the large diffeomorphic deformation metric mapping (LDDMM) framework for surface mapping and show improvements in speed and accuracy via a multiresolution analysis of surface meshes and the construction of multiresolution diffeomorphic transformations. The proposed method constructs a family of multiresolution meshes that are used as natural sparse priors of the cortical morphology. At varying resolutions, these meshes act as anchor points where the parameterization of multiresolution deformation vector fields can be supported, allowing the construction of a bundle of multiresolution deformation fields, each originating from a different resolution. Using a coarse-to-fine approach, we show a potential reduction in computation cost along with improvements in sulcal alignment when compared with LDDMM surface mapping.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2016.2574982