title: Groupwise registration by hierarchical anatomical correspondence detection.
abstract: We present a novel feature-based groupwise registration method to simultaneously warp the subjects towards the common space. Due to the complexity of the groupwise registration, we resort to decoupling it into two easy-to-solve tasks, i.e., alternatively establishing the robust correspondences across different subjects and interpolating the dense deformation fields based on the detected sparse correspondences. Specifically, several novel strategies are proposed in the correspondence detection step. First, attribute vector, instead of intensity only, is used as a morphological signature to guide the anatomical correspondence detection among all subjects. Second, we detect correspondence only on the driving voxels with distinctive attribute vectors for avoiding the ambiguity in detecting correspondences for non-distinctive voxels. Third, soft correspondence assignment (allowing for adaptive detection of multiple correspondences in each subject) is also presented to help establish reliable correspondences across all subjects, which is particularly necessary in the beginning of groupwise registration. Based on the sparse correspondences detected on the driving voxels of each subject, thin-plate splines (TPS) are then used to propagate the correspondences on the driving voxels to the entire brain image for estimating the dense transformation for each subject. By iteratively repeating correspondence detection and dense transformation estimation, all the subjects will be aligned onto a common space simultaneously. Our groupwise registration algorithm has been extensively evaluated by 18 elderly brains, 16 NIREP, and 40 LONI data. In all experiments, our algorithm achieves more robust and accurate registration results, compared to a groupwise registration method and a pairwise registration method, respectively.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA. grwu@med.unc.edu
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Hongjun
authors.initials: H
authors.lastname: Jia
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
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