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Reference-free brain template construction with population symmetric registration

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Abstract

Population registration has been proposed for normalizing a large group of images into a common space, which is important in many clinical and research studies, such as brain development, aging, and atlas construction. Different from pairwise registration problem that aligns the target image to the reference directly, determining the reference or the hidden common space with the least bias is important in population registration. In order to decrease this bias, a lot of work takes the arithmetic mean image as the reference. However, the arithmetic mean image is usually too smooth to guide the population registration. This work presents an efficient symmetric population registration strategy for brain template construction, which defines the symmetric population center guiding population registration. This is important because the population registration problem can be translated into a series of pairwise registration problem which is easier to optimize and implement. Another prominent merit of proposed population registration algorithm is reference-free, which eliminates the reference dependency–related bias in population registration. Based on this symmetric population registration, the brain template is constructed by approximating both the population’s intensity and gradient information. In addition, we also present a new measurement named with average bias for evaluating the unbiasedness of brain template. Experiments were first carried out on four synthetic images created with controllable transforms, which aim at comparing the difference between conventional method and proposed method. Further experiment is designed for reference-free validation. Finally, in real inter-subject brain data, twenty MRI T1 volumes with size 256 × 256 × 176 are used to construct a symmetric brain template with proposed population registration method. The constructed brain template has a small bias and clear brain details comparing with DARTEL.

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References

  1. Wang F, Vemuri BC, Rangariajan A, Schmalfuss IM, Eisenschenk SJ (2008) Simultaneous nonrigid registration of multiple point sets and atlas construction. IEEE Trans Pattern Anal Mach Intell 30:2011–2022

    Article  Google Scholar 

  2. Baloch S, Davatzikos C (2009) Morphological appearance manifold in computational anatomy: groupwise registration and morphological analysis. NeuroImage 45:S73–S85

    Article  Google Scholar 

  3. Viceic D, Campos R, Eleonora F, Spiere L, Meuli R, Clarke S, Thiran J-P (2009) Local landmark-based registration for fMRI group studies of nonprimary auditory cortex. NeuroImage 44:145–153

    Article  Google Scholar 

  4. Avants B,Cook PA, McMillan C, Grossman M, Tutison NJ, Zheng Y, Gee JC (2010) Sparse unbiased analysis of anatomical variance in longitudinal imaging. Jiang T et al. (eds) MICCAI 2010, Part I, LNCS 6361, pp 324–331

  5. Zhang Y, Zhang J, Hsu J, Oishi K, Faria AV, Albert M, Miller MI, Mori S (2014) Evaluation of group-specific, whole-brain atlas generation using volume-based template estimation (VTE): application to normal and Alzheimer’s populations. NeuroImage 84:406–419

    Article  Google Scholar 

  6. Peng H, Orlichenko A, Dawe RJ, Agam G, Zhang S, Arfanakis K (2009) Development of a human brain diffusion tensor template. NeuroImage 46:967–980

    Article  Google Scholar 

  7. Cheng G, Vemuri BC, Hwang M-S, Howland D, Forder JR (2011) Atlas construction from high angular resolution diffusion imaging data represented gaussian mixture fields. Int Symp Biomed Imaging:549–552

  8. Ali G, Catherine L, Sean C, Cedric C, Alieza A-A, Estroff JA, Warfield SK (2014) Construction of deformable spatiotemporal MRI atlas of the fetal brain: evaluation of similarity metrics and deformable models. Golland P et al. (eds) MICCAI 2014, Part II, LNCS 8674, pp 292–299

  9. Twining CJ, Marsland S (2004) Groupwise non-rigid registration: the minimum description length approach, In Processings of British Machine Vision Conference, pp 81–84

  10. Zollei L, Learned-miller EG, Grimson E, Wells W (2005) Efficient population registration of 3D data. International Conference on Computer Vision for Biomedical Image Applications (ICCV), LNCS 3765. pp 291–301

  11. Tang Z, Tang Y (2014) Groupwise registration of brain establishing accurate spatial correspondence of brain structures. In: Li S and Tavares JMR (eds) Shape Analysis in Medical Image Analysis, Lecture Notes in Computational Vision and Biomechanics 14, pp 229–257

  12. Guimond A, Meunier J, Thirion J-P Average brain models: a convergence study. Comput Vision Image Underst 77(2000):192–210

  13. Seghers D, D'Agostino E, Maes F, Vandermeulen D, Suetens P (2004) Construction of a brain template from MR images using state-of-the-art registration and segmentation techniques. In: Barillot C, Haynor DR, Gellier P (eds) MICCAI 2004, LNCS 3216, pp 696–708

  14. Park H, Bland PH, Hero AO, Meyer CR (2005) Least biased target seection in probabilistic atlas construction. In: Duncan J, Gerig G (eds) MICCAI 2005, LNCS 3750, pp 419–426

  15. Joshi S, Davis B, Jomier M, Gerig G (2004) Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23:S151–S160

    Article  Google Scholar 

  16. Geng X, Christensen GE, Hong G, Ross TJ, Yang Y (2009) Implicit reference-based group-wise image registration and its application to structural and functional MRI. NeuroImage 47:1341–1351

    Article  Google Scholar 

  17. Wang Q, Wu G, Yap P-T, Shen D (2010) Attribute vector guided groupwise registration. NeuroImage 50:1485–1496

    Article  Google Scholar 

  18. Taylor CJ, Babalola KO, Petrovic VS, Twining CJ, Cootes TF (2010) Computing accurate correspondences across groups of images. IEEE Trans Pattern Anal Mach Intell 32:1994–2005

    Article  Google Scholar 

  19. Wu G, Jia H, Wang Q, Shen D (2011) SharpMean: groupwise registration guided by sharp mean image and tree-based registration. NeuroImage 56:1968–1981

    Article  Google Scholar 

  20. Wu G, Wang Q, Jia H, Shen D (2012) Feature-based groupwise registration by hierarchical anatomical correspondence detection. Hum Brain Mapp 33:253–271

    Article  Google Scholar 

  21. Igor Y, Thompson PM., Osher S, Leow AD (2007) Topology preserving log-unbiased nonlinear image registration: theory and implementation. IEEE Conference on Computer Vision and Pattern Recognition

  22. Tang S, Fan Y, Wu G, Kim M, Shen D (2009) RABBIT: rapid alignment of brains by building intermediate templates. NeuroImage 47:1277–1287

    Article  Google Scholar 

  23. Wang Q, Chen L, Yap P-T, Wu G, Shen D (2010) Groupwise registration based on hierarchical image clustering and atlas synthesis. Hum Brain Mapp 31:1128–1140

    Article  Google Scholar 

  24. Jia H, Yap P-T, Wu G, Wang Q, Shen D (2011) Intermedicate template guided groupwise registration of diffusion tensor images. NeuroImage 54:928–939

    Article  Google Scholar 

  25. Ying S, Wu G, Wang Q, Shen D (2014) Hierarchical unbiased graph shrinkage (HUGS): a noval groupwise registration for large data set. NeuroImage 84:626–638

    Article  Google Scholar 

  26. Wang Y, Farnebäck G, Westin C-F (2010) Multi-affine registration using local polynomial expansion. J Zhejiang Univ-Sci C 11:495–503

    CAS  Google Scholar 

  27. Wang Y, Chen Z, Nie S, Westin C-F (2013) Diffusion tensor image registration using polynomial expansion. Phys Med Biol 58:6029–6046

    Article  Google Scholar 

  28. Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260

    Article  CAS  Google Scholar 

  29. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156

    Article  CAS  Google Scholar 

  30. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17:825–841

    Article  Google Scholar 

  31. Greve DN, Fischl B (2009) Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48:63–72

    Article  Google Scholar 

  32. John A (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95–113

    Article  Google Scholar 

Download references

Acknowledgments

We are thankful to Prof. Carl-Fredrik Westin and Lipeng Ning at Harvard Medical School for their help in this research and manuscript writing.

Funding

This work is sponsored by the Natural Science Foundation of Shanghai (18ZR1426900) and the National Natural Science Foundation of China (61201067).

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Authors and Affiliations

  1. Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Yuanjun Wang & Fan Jiang

  2. Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China

    Yu Liu

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  1. Yuanjun Wang
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  2. Fan Jiang
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  3. Yu Liu
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Correspondence to Yuanjun Wang or Yu Liu.

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Wang, Y., Jiang, F. & Liu, Y. Reference-free brain template construction with population symmetric registration. Med Biol Eng Comput 58, 2083–2093 (2020). https://doi.org/10.1007/s11517-020-02226-5

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  • DOI: https://doi.org/10.1007/s11517-020-02226-5

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