Advertisement

Plug-and-Play Priors for Reconstruction-Based Placental Image Registration

  • Jiarui XingEmail author
  • Ulugbek Kamilov
  • Wenjie Wu
  • Yong Wang
  • Miaomiao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)

Abstract

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.

Notes

Acknowledgement

This work was supported by NIH grant R01HD094381, NIH grant R01AG053548, and BrightFocus Foundation A2017330S.

References

  1. 1.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  2. 2.
    Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18(11), 2419–2434 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vision 61(2), 139–157 (2005)CrossRefGoogle Scholar
  4. 4.
    Blencowe, H., et al.: Born too soon: the global epidemiology of 15 million preterm births. Reprod. Health 10(1), S2 (2013)CrossRefGoogle Scholar
  5. 5.
    Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM J. Imaging Sci. 3(3), 492–526 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Broit, C.: Optimal registration of deformed images (1981)Google Scholar
  7. 7.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65. IEEE (2005)Google Scholar
  8. 8.
    Buzzard, G.T., Chan, S.H., Sreehari, S., Bouman, C.A.: Plug-and-play unplugged: optimization free reconstruction using consensus equilibrium. SIAM J. Imaging Sci. 11(3), 2001–2020 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chan, S.H., Wang, X., Elgendy, O.A.: Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE Trans. Comput. Imaging 3(1), 84–98 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Christensen, G.E.: Deformable shape models for anatomy (1994)Google Scholar
  11. 11.
    Dabov, K., Foi, A., Egiazarian, K.: Video denoising by sparse 3D transform-domain collaborative filtering. In: 15th European Signal Processing Conference, pp. 145–149. IEEE (2007)Google Scholar
  12. 12.
    Han, J., et al.: A variational framework for joint image registration, denoising and edge detection. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2006, pp. 246–250. Springer, Heidelberg (2006).  https://doi.org/10.1007/3-540-32137-3_50CrossRefGoogle Scholar
  13. 13.
    Le Bihan, D., Poupon, C., Amadon, A., Lethimonnier, F.: Artifacts and pitfalls in diffusion MRI. J. Magn. Reson. Imaging 24(3), 478–488 (2006). An Official Journal of the International Society for Magnetic Resonance in MedicineCrossRefGoogle Scholar
  14. 14.
    Lempitsky, V., Rother, C., Blake, A.: Logcut-efficient graph cut optimization for Markov random fields. In: IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (2007)Google Scholar
  15. 15.
    Leventon, M., Wells III, W.M., Grimson, W.E.L.: Multiple view 2D-3D mutual information registration. In: Image Understanding Workshop, vol. 20, p. 21. Citeseer (1997)Google Scholar
  16. 16.
    Lombaert, H., Cheriet, F.: Simultaneous image de-noising and registration using graph cuts: application to corrupted medical images. In: 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), pp. 264–268. IEEE (2012)Google Scholar
  17. 17.
    Lombaert, H., Cheriet, F.: Simultaneous image denoising and registration using graph cuts, July 2012Google Scholar
  18. 18.
    Meinhardt, T., Moeller, M., Hazirbas, C., Cremers, D.: Learning proximal operators: using denoising networks for regularizing inverse imaging problems. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Venice, pp. 1799–1808, 22–29 October 2017Google Scholar
  19. 19.
    Miller, M.I., Trouvé, A., Younes, L.: Geodesic shooting for computational anatomy. J. Math. Imaging Vis. 24(2), 209–228 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006).  https://doi.org/10.1007/978-0-387-40065-5CrossRefzbMATHGoogle Scholar
  21. 21.
    Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1(3), 123–231 (2014)Google Scholar
  22. 22.
    Partridge, S.C., McDonald, E.S.: Diffusion weighted MRI of the breast: protocol optimization, guidelines for interpretation, and potential clinical applications. Magn. Reson. Imaging Clin. N. Am. 21(3), 601 (2013)CrossRefGoogle Scholar
  23. 23.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Ryu, E.K., Liu, J., Wnag, S., Chen, X., Wang, Z., Yin, W.: Plug-and-play methods provably converge with properly trained denoisers. In: Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, June 2019Google Scholar
  25. 25.
    Sanches, J.M., Marques, J.S.: Joint image registration and volume reconstruction for 3D ultrasound. Pattern Recogn. Lett. 24(4–5), 791–800 (2003)CrossRefGoogle Scholar
  26. 26.
    Sreehari, S., et al.: Plug-and-play priors for bright field electron tomography and sparse interpolation. IEEE Trans. Comput. Imaging 2(4), 408–423 (2016)MathSciNetGoogle Scholar
  27. 27.
    Sun, Y., Wohlberg, B., Kamilov, U.S.: An online plug-and-play algorithm for regularized image reconstruction. IEEE Trans. Comput. Imaging (2019) Google Scholar
  28. 28.
    Telea, A., Preusser, T., Garbe, C., Droske, M., Rumpf, M.: A variational approach to joint denoising, edge detection and motion estimation. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 525–535. Springer, Heidelberg (2006).  https://doi.org/10.1007/11861898_53CrossRefGoogle Scholar
  29. 29.
    Tomaževič, D., Likar, B., Pernuš, F.: Reconstruction-based 3D/2D image registration. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 231–238. Springer, Heidelberg (2005).  https://doi.org/10.1007/11566489_29CrossRefGoogle Scholar
  30. 30.
    Venkatakrishnan, S.V., Bouman, C.A., Wohlberg, B.: Plug-and-play priors for model based reconstruction. In: IEEE Global Conference on Signal and Information Processing, pp. 945–948. IEEE (2013)Google Scholar
  31. 31.
    Vialard, F.X., Risser, L., Rueckert, D., Cotter, C.J.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. Int. J. Comput. Vision 97(2), 229–241 (2012)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Vishnevskiy, V., Stoeck, C., Székely, G., Tanner, C., Kozerke, S.: Simultaneous denoising and registration for accurate cardiac diffusion tensor reconstruction from MRI. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 215–222. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_27CrossRefGoogle Scholar
  33. 33.
    Zhang, M., Fletcher, P.T.: Finite-dimensional lie algebras for fast diffeomorphic image registration. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 249–260. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19992-4_19CrossRefGoogle Scholar
  34. 34.
    Zhang, M., Fletcher, P.T.: Fast diffeomorphic image registration via Fourier-approximated lie algebras. Int. J. Comput. Vision 127(1), 61–73 (2019)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zhang, M., et al.: Frequency diffeomorphisms for efficient image registration. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 559–570. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59050-9_44CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiarui Xing
    • 1
    Email author
  • Ulugbek Kamilov
    • 2
    • 3
  • Wenjie Wu
    • 4
    • 5
  • Yong Wang
    • 5
  • Miaomiao Zhang
    • 1
    • 6
  1. 1.Electrical and Computer EngineeringUniversity of VirginiaCharlottesvilleUSA
  2. 2.Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA
  3. 3.Electrical and Systems EngineeringWashington University in St. LouisSt. LouisUSA
  4. 4.Biomedical EngineeringWashington University in St. LouisSt. LouisUSA
  5. 5.Obstetrics and GynecologyWashington University in St. LouisSt. LouisUSA
  6. 6.Computer ScienceUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations