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Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans

  • Xu HanEmail author
  • Spyridon Bakas
  • Roland Kwitt
  • Stephen Aylward
  • Hamed Akbari
  • Michel Bilello
  • Christos Davatzikos
  • Marc Niethammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Registering brain magnetic resonance imaging (MRI) scans containing pathologies is challenging primarily due to large deformations caused by the pathologies, leading to missing correspondences between scans. However, the registration task is important and directly related to personalized medicine, as registering between baseline pre-operative and post-recurrence scans may allow the evaluation of tumor infiltration and recurrence. While many registration methods exist, most of them do not specifically account for pathologies. Here, we propose a framework for the registration of longitudinal image-pairs of individual patients diagnosed with glioblastoma. Specifically, we present a combined image registration/reconstruction approach, which makes use of a patient-specific principal component analysis (PCA) model of image appearance to register baseline pre-operative and post-recurrence brain tumor scans. Our approach uses the post-recurrence scan to construct a patient-specific model, which then guides the registration of the pre-operative scan. Quantitative and qualitative evaluations of our framework on 10 patient image-pairs indicate that it provides excellent registration performance without requiring (1) any human intervention or (2) prior knowledge of tumor location, growth or appearance.

Notes

Acknowledgements

Research reported in this publication was supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF), under award numbers NIH:2R44NS081792, NIH/NINDS:R01NS042645, NIH/NCI:U24CA189523, NSF/ECCS-1148870, and NSF/EECS-1711776. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, or the NSF.

References

  1. 1.
    Price, S.J., Jena, R., Burnet, N.G., Carpenter, T.A., Pickard, J.D., Gillard, J.H.: Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur. Radiol. 17(7), 1675–1684 (2007)CrossRefGoogle Scholar
  2. 2.
    Milano, M.T., et al.: Patterns and timing of recurrence after temozolomide-based chemoradiation for glioblastoma. Int. J. Rad. Oncol. Biol. Phys. 78(4), 1147–1155 (2010)CrossRefGoogle Scholar
  3. 3.
    Brett, M., Leff, A.P., Rorden, C., Ashburner, J.: Spatial normalization of brain images with focal lesions using cost function masking. NeuroImage 14(2), 486–500 (2001)CrossRefGoogle Scholar
  4. 4.
    Niethammer, M., et al.: Geometric metamorphosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 639–646. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23629-7_78CrossRefGoogle Scholar
  5. 5.
    Gooya, A., et al.: GLISTR: glioma image segmentation and registration. IEEE TMI 31(10), 1941–1954 (2012)Google Scholar
  6. 6.
    Bakas, S., et al.: GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 144–155. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30858-6_13CrossRefGoogle Scholar
  7. 7.
    Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.M.: PORTR: pre-operative and post-recurrence brain tumor registration. IEEE TMI 33(3), 651–667 (2014)Google Scholar
  8. 8.
    Liu, X., Niethammer, M., Kwitt, R., Singh, N., McCormick, M., Aylward, S.: Low-rank atlas image analyses in the presence of pathologies. IEEE TMI 34(12), 2583–2591 (2015)Google Scholar
  9. 9.
    Yang, X., Han, X., Park, E., Aylward, S., Kwitt, R., Niethammer, M.: Registration of pathological images. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 97–107. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46630-9_10CrossRefGoogle Scholar
  10. 10.
    Han, X., Yang, X., Aylward, S., Kwitt, R., Niethammer, M.: Efficient registration of pathological images: a joint PCA/image-reconstruction approach. In: ISBI (2017)Google Scholar
  11. 11.
    Kwon, D., Zeng, K., Bilello, M., Davatzikos, C.: Estimating patient specific templates for pre-operative and follow-up brain tumor registration. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 222–229. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24571-3_27CrossRefGoogle Scholar
  12. 12.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  15. 15.
    Modat, M., et al.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98(3), 278–284 (2010)CrossRefGoogle Scholar
  16. 16.
    Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imaging 1(2), 024003 (2014)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: Dramms: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622–639 (2011)CrossRefGoogle Scholar
  19. 19.
    Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xu Han
    • 1
    Email author
  • Spyridon Bakas
    • 2
  • Roland Kwitt
    • 3
  • Stephen Aylward
    • 4
  • Hamed Akbari
    • 2
  • Michel Bilello
    • 2
  • Christos Davatzikos
    • 2
  • Marc Niethammer
    • 1
  1. 1.Department of Computer ScienceUNC Chapel HillChapel HillUSA
  2. 2.Center for Biomedical Image Computing and Analytics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.University of SalzburgSalzburgAustria
  4. 4.Kitware Inc.New YorkUSA

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