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.
journal_name
Magn Reson Imagingjournal_title
Magnetic resonance imagingauthors
Wen Y,Hou L,He L,Peterson BS,Xu Ddoi
10.1016/j.mri.2015.01.013subject
Has Abstractpub_date
2015-05-01 00:00:00pages
465-73issue
4eissn
0730-725Xissn
1873-5894pii
S0730-725X(15)00018-1journal_volume
33pub_type
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