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Efficient Groupwise Registration for Brain MRI by Fast Initialization

  • Pei Dong
  • Xiaohuan Cao
  • Jun Zhang
  • Minjeong Kim
  • Guorong Wu
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10541)

Abstract

Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps. First, a set of training brain MR images is registered to their group center with a certain existing groupwise registration method. Then, in order to augment the samples, we perform PCA on the set of obtained deformation fields (to the group center) to parameterize the deformation fields. In doing so, we can generate a large number of deformation fields, as well as their respective simulated samples using different parameters for PCA. In the application stage, when given a new set of testing brain MR images, we can mix them with the augmented training samples. Then, for each testing image, we can find its closest sample in the augmented training dataset for fast estimating its deformation field to the group center of the training set. In this way, a tentative group center of the testing image set can be immediately estimated, and the deformation field of each testing image to this estimated group center can be obtained. With this fast initialization for groupwise registration of testing images, we can finally use an existing groupwise registration method to quickly refine the groupwise registration results. Experimental results on ADNI dataset show the significantly improved computational efficiency and competitive registration accuracy, compared to state-of-the-art groupwise registration methods.

References

  1. 1.
    Viergever, M.A., Maintz, J.B.A., Klein, S., et al.: A survey of medical image registration – under review. Med. Image Anal. 33, 140–144 (2016)CrossRefGoogle Scholar
  2. 2.
    Joshi, S., Davis, B., Jomier, M., et al.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(Suppl. 1), S151–S160 (2004)CrossRefGoogle Scholar
  3. 3.
    Ying, S., Wu, G., Wang, Q., et al.: Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set. NeuroImage 84, 626–638 (2014)CrossRefGoogle Scholar
  4. 4.
    Wu, G., Peng, X., Ying, S., et al.: eHUGS: enhanced hierarchical unbiased graph shrinkage for efficient groupwise registration. PLoS ONE 11, e0146870 (2016)CrossRefGoogle Scholar
  5. 5.
    Xue, Z., Shen, D., Karacali, B., et al.: Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms. NeuroImage 33, 855–866 (2006)CrossRefGoogle Scholar
  6. 6.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87–97 (1998)CrossRefGoogle Scholar
  7. 7.
    Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001)CrossRefGoogle Scholar
  9. 9.
    Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pei Dong
    • 1
  • Xiaohuan Cao
    • 1
  • Jun Zhang
    • 1
  • Minjeong Kim
    • 1
  • Guorong Wu
    • 1
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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