title: Fast tensor image morphing for elastic registration.
abstract: We propose a novel algorithm, called Fast Tensor Image Morphing for Elastic Registration or F-TIMER. F-TIMER leverages multiscale tensor regional distributions and local boundaries for hierarchically driving deformable matching of tensor image volumes. Registration is achieved by aligning a set of automatically determined structural landmarks, via solving a soft correspondence problem. Based on the estimated correspondences, thin-plate splines are employed to generate a smooth, topology preserving, and dense transformation, and to avoid arbitrary mapping of non-landmark voxels. To mitigate the problem of local minima, which is common in the estimation of high dimensional transformations, we employ a hierarchical strategy where a small subset of voxels with more distinctive attribute vectors are first deployed as landmarks to estimate a relatively robust low-degrees-of-freedom transformation. As the registration progresses, an increasing number of voxels are permitted to participate in refining the correspondence matching. A scheme as such allows less conservative progression of the correspondence matching towards the optimal solution, and hence results in a faster matching speed. Results indicate that better accuracy can be achieved by F-TIMER, compared with other deformable registration algorithms, with significantly reduced computation time cost of 4-14 folds.
authors.affiliation: Department of Radiology, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D054459:Elasticity Imaging Techniques / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique