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Diffeomorphic Density Registration in Thoracic Computed Tomography

  • Caleb RottmanEmail author
  • Ben Larson
  • Pouya Sabouri
  • Amit Sawant
  • Sarang Joshi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Accurate motion estimation in thoracic computed tomography (CT) plays a crucial role in the diagnosis and treatment planning of lung cancer. This paper provides two key contributions to this motion estimation. First, we show we can effectively transform a CT image of effective linear attenuation coefficients to act as a density, i.e. exhibiting conservation of mass while undergoing a deformation. Second, we propose a method for diffeomorphic density registration for thoracic CT images. This algorithm uses the appropriate density action of the diffeomorphism group while offering a weighted penalty on local tissue compressibility. This algorithm appropriately models highly compressible areas of the body (such as the lungs) and incompressible areas (such as surrounding soft tissue and bones).

Keywords

Diffeomorphisms Thoracic motion estimation Density action Image registration 

References

  1. 1.
    CDC - basic information about lung cancer. http://www.cdc.gov/cancer/lung/basic_info/index.htm. Accessed 06 Mar 2016
  2. 2.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)CrossRefGoogle Scholar
  3. 3.
    Bauer, M., Bruveris, M., Michor, P.W.: Uniqueness of the Fisher-Rao metric on the space of smooth densities. submitted (2015)Google Scholar
  4. 4.
    Bauer, M., Joshi, S., Modin, K.: Diffeomorphic density matching by optimal information transport, pp. 1–35 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)CrossRefGoogle Scholar
  6. 6.
    Boone, J.M., Seibert, J.A.: An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kv. Med. phys. 24(11), 1661–1670 (1997)CrossRefGoogle Scholar
  7. 7.
    Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849–1870 (2009)CrossRefGoogle Scholar
  8. 8.
    Geneser, S.E., Hinkle, J.D., Kirby, R.M., Wang, B., Salter, B., Joshi, S.: Quantifying variability in radiation dose due to respiratory-induced tumor motion. Med. Image Anal. 15(4), 640–649 (2011)CrossRefGoogle Scholar
  9. 9.
    Gorbunova, V., Sporring, J., Lo, P., Loeve, M., Tiddens, H.A., Nielsen, M., Dirksen, A., de Bruijne, M.: Mass preserving image registration for lung CT. Med. Image Anal. 16(4), 786–795 (2012)CrossRefGoogle Scholar
  10. 10.
    Rottman, C., Bauer, M., Modin, K., Joshi, S.C.: Weighted diffeomorphic density matching with applications to thoracic image registration. In: 5th MICCAI Workshop on Mathematical Foundations of Computational Anatomy (MFCA 2015), pp. 1–12 (2015)Google Scholar
  11. 11.
    Sawant, A., Keall, P., Pauly, K.B., Alley, M., Vasanawala, S., Loo, B.W., Hinkle, J., Joshi, S.: Investigating the feasibility of rapid MRI for image-guided motion management in lung cancer radiotherapy. BioMed. Res. Int. 2014 (2014)CrossRefGoogle Scholar
  12. 12.
    Yin, Y., Hoffman, E.A., Lin, C.L.: Mass preserving nonrigid registration of CT lung images using cubic B-spline. Med. Phys. 36(9), 4213–4222 (2009)CrossRefGoogle Scholar
  13. 13.
    Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Caleb Rottman
    • 1
    Email author
  • Ben Larson
    • 1
  • Pouya Sabouri
    • 2
  • Amit Sawant
    • 2
  • Sarang Joshi
    • 1
  1. 1.Scientific Computing and Imaging Institute, Department of BioengineeringUniversity of UtahSalt Lake CityUSA
  2. 2.University of Maryland School of MedicineBaltimoreUSA

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