A Robust Probabilistic Model for Motion Layer Separation in X-ray Fluoroscopy

  • Peter Fischer
  • Thomas Pohl
  • Thomas Köhler
  • Andreas Maier
  • Joachim Hornegger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

Fluoroscopic images are characterized by a transparent projection of 3-D structures from all depths to 2-D. Differently moving structures, for example due to breathing and heartbeat, can be described approximately using independently moving 2-D layers. Separating the fluoroscopic images into the motion layers is desirable to facilitate interpretation and diagnosis. Given the motion of each layer, it is state of the art to compute the layer separation by minimizing a least-squares objective function. However, due to high noise levels and inaccurate motion estimates, the results are not satisfactory in X-ray images.

In this work, we propose a probabilistic model for motion layer separation. In this model, we analyze various data terms and regularization terms theoretically and experimentally. We show that a robust penalty function is required in the data term to deal with noise and shortcomings of the image formation model. For the regularization term, we propose to enforce smoothness of the layers using bilateral total variation. On synthetic data, the mean squared error between the estimated layers and the ground truth is improved by \(18\,\%\) compared to the state of the art. In addition, we show qualitative improvements on real X-ray data.

References

  1. 1.
    Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63(1), 75–104 (1996)CrossRefGoogle Scholar
  2. 2.
    Cao, Y., Wang, P.: An adaptive method of tracking anatomical curves in X-ray sequences. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 173–180. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  3. 3.
    Close, R.A., Abbey, C.K., Morioka, C.A., Whiting, J.S.: Accuracy assessment of layer decomposition using simulated angiographic image sequences. IEEE Trans. Med. Imaging 20(10), 990–998 (2001)CrossRefGoogle Scholar
  4. 4.
    Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004)CrossRefGoogle Scholar
  5. 5.
    Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Heibel, H., Glocker, B., Groher, M., Pfister, M., Navab, N.: Interventional tool tracking using discrete optimization. IEEE Trans. Med. Imaging 32(3), 544–555 (2013)CrossRefGoogle Scholar
  7. 7.
    Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. Int. J. Comput. Vision 50(3), 329–343 (2002)MATHCrossRefGoogle Scholar
  8. 8.
    Maier, A., Hofmann, H., Berger, M., Fischer, P., Schwemmer, C., Wu, H., Müller, K., Hornegger, J., Choi, J.H., Riess, C., Keil, A., Fahrig, R.: CONRAD-a software framework for cone- beam imaging in radiology. Med. Phys. 40(11) (2013)Google Scholar
  9. 9.
    Manhart, M., Kowarschik, M., Fieselmann, A., Deuerling-Zheng, Y., Royalty, K., Maier, A., Hornegger, J.: Dynamic iterative reconstruction for interventional 4-D c-arm CT perfusion imaging. IEEE Trans. Med. Imaging 32(7), 1336–1348 (2013)CrossRefGoogle Scholar
  10. 10.
    Preston, J.S., Rottman, C., Cheryauka, A., Anderton, L., Whitaker, R.T., Joshi, S.: Multi-layer deformation estimation for fluoroscopic imaging. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 123–134. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  11. 11.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(14), 259–268 (1992)MATHCrossRefGoogle Scholar
  12. 12.
    Segars, W., Mahesh, M., Beck, T., Frey, E., Tsui, B.: Realistic CT simulation using the 4D XCAT phantom. Med. Phys. 35(8), 3800–3808 (2008)CrossRefGoogle Scholar
  13. 13.
    Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vision 106(2), 115–137 (2014)CrossRefGoogle Scholar
  14. 14.
    Szeliski, R., Avidan, S., Anandan, P.: Layer extraction from multiple images containing reflections and transparency. In: CVPR, vol. 1, pp. 246–253. IEEE (2000)Google Scholar
  15. 15.
    Tipping, M.E., Bishop, C.M.: Bayesian image super-resolution. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 1303–1310. MIT Press, Cambridge (2003)Google Scholar
  16. 16.
    Weiss, Y.: Deriving intrinsic images from image sequences. In: ICCV, vol. 2, pp. 68–75. IEEE (2001)Google Scholar
  17. 17.
    Zhang, W., Ling, H., Prummer, S., Zhou, K.S., Ostermeier, M., Comaniciu, D.: Coronary tree extraction using motion layer separation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 116–123. Springer, Heidelberg (2009) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Fischer
    • 1
  • Thomas Pohl
    • 2
  • Thomas Köhler
    • 1
  • Andreas Maier
    • 1
  • Joachim Hornegger
    • 1
  1. 1.Pattern Recognition Lab and Erlangen Graduate School in Advanced Optical Technologies (SAOT)FAU Erlangen-NürnbergErlangenGermany
  2. 2.Siemens HealthcareForchheimGermany

Personalised recommendations