title: A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.
abstract: Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention.
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing & Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China. Electronic address: ywen@cs.ecnu.edu.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wen
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing & Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China.
authors.forename: Lili
authors.initials: L
authors.lastname: Hou
authors.affiliation: Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 200092, China.
authors.forename: Lianghua
authors.initials: L
authors.lastname: He
authors.affiliation: Institute for the Developing Mind, Children's Hospital Los Angeles, The Keck School of Medicine at the University of Southern California, Los Angeles, CA 90027, U.S.A.. Electronic address: bspmd61@gmail.com.
authors.forename: Bradley S
authors.initials: BS
authors.lastname: Peterson
authors.affiliation: MRI Unit & Epidemiology Division, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, 10032, U.S.A.. Electronic address: dx2103@columbia.edu.
authors.forename: Dongrong
authors.initials: D
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D017711:Nonlinear Dynamics; D057849:Optic Flow; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D013382:Subtraction Technique
keywords: Brain image normalization; Hierarchical strategy; Optical flow constraints; Symmetric optical flow