International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 579-586

Estimate, Compensate, Iterate: Joint Motion Estimation and Compensation in 4-D Cardiac C-arm Computed Tomography

  • Oliver Taubmann
  • Günter Lauritsch
  • Andreas Maier
  • Rebecca Fahrig
  • Joachim Hornegger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

C-arm computed tomography reconstruction of multiple cardiac phases could provide a highly useful tool to interventional cardiologists in the catheter laboratory. Today, however, for clinically reasonable acquisition protocols the achievable image quality is still severely limited due to undersampling artifacts. We propose an iterative optimization scheme combining image registration, motion compensation and spatio-temporal regularization to improve upon the state-of-the-art w.r.t. image quality and accuracy of motion estimation. Evaluation of clinical cases indicates an improved visual appearance and temporal consistency, evidenced by a strong decrease in temporal variance in uncontrasted regions accompanied by an increased sharpness of the contrasted left ventricular blood pool boundary. In a phantom study, the universal image quality index proposed by Wang et al. is raised from 0.80 to 0.95, with 1.0 corresponding to a perfect match with the ground truth. The results lay a promising foundation for interventional cardiac functional analysis.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    De Buck, S., Dauwe, D., Wielandts, J.Y., Claus, P., Janssens, S., Heidbuchel, H., Nuyens, D.: A new approach for prospectively gated cardiac rotational angiography. Proc. SPIE 8668 (2013)Google Scholar
  2. 2.
    Döring, M., Braunschweig, F., Eitel, C., Gaspar, T., Wetzel, U., Nitsche, B., Hindricks, G., Piorkowski, C.: Individually tailored left ventricular lead placement: lessons from multimodality integration between three-dimensional echocardiography and coronary sinus angiogram. Europace 15(5), 718–727 (2013)CrossRefGoogle Scholar
  3. 3.
    Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: elastix: a toolbox for intensity based medical image registration. IEEE Trans. Med. Imag. 29(1), 196–205 (2010)CrossRefGoogle Scholar
  4. 4.
    Lauritsch, G., Boese, J., Wigstrom, L., Kemeth, H., Fahrig, R.: Towards cardiac C-arm computed tomography. IEEE Trans. Med. Imag. 25(7), 922–934 (2006)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Maier, A., Taubmann, O., Wetzl, J., Wasza, J., Forman, C., Fischer, P., Hornegger, J., Fahrig, R.: Fast Interpolation of Dense Motion Fields from Synthetic Phantoms. In: BVM, pp. 168–173 (2014)Google Scholar
  7. 7.
    Mc Kinnon, G.C., Bates, R.: Towards imaging the beating heart usefully with a conventional CT scanner. IEEE Trans. Biomed. Eng. BME 28(2), 123–127 (1981)CrossRefGoogle Scholar
  8. 8.
    Mory, C., Auvray, V., Zhang, B., Grass, M., Schäfer, D., Chen, S., Carroll, J., Rit, S., Peyrin, F., Douek, P., Boussel, L.: Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization. Med. Phys. 41, 021903 (2014)Google Scholar
  9. 9.
    Müller, K., Lauritsch, G., Schwemmer, C., Maier, A., Taubmann, O., Abt, B., Köhler, H., Nöttling, A., Hornegger, J., Fahrig, R.: Catheter artifact reduction (CAR) in dynamic cardiac chamber imaging with interventional C-arm CT. In: Proc. Intl. Conf. on Image Formation in X-ray CT, pp. 418–421 (2014)Google Scholar
  10. 10.
    Müller, K., Maier, A., Schwemmer, C., Lauritsch, G., Buck, S.D., Wielandts, J.Y., Hornegger, J., Fahrig, R.: Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT. Phys. Med. Biol. 59(12), 3121–3138 (2014)CrossRefGoogle Scholar
  11. 11.
    Schäfer, D., Borgert, J., Rasche, V., Grass, M.: Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography. IEEE Trans. Med. Imag. 25, 898–906 (2006)CrossRefGoogle Scholar
  12. 12.
    Segars, W.P., Sturgeon, G., Mendonca, S., Grimes, J., Tsui, B.M.W.: 4D XCAT. phantom for multimodality imaging research. Med. Phys. 37, 4902–4915 (2010)Google Scholar
  13. 13.
    Taubmann, O., Wetzl, J., Lauritsch, G., Maier, A., Hornegger, J.: Sharp as a Tack: Measuring and Comparing Edge Sharpness in Motion-Compensated Medical Image Reconstruction. In: BVM, pp. 425–430 (2015)Google Scholar
  14. 14.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846 (1998)Google Scholar
  15. 15.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Proc. Let. 9(3), 81–84 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oliver Taubmann
    • 1
    • 2
  • Günter Lauritsch
    • 3
  • Andreas Maier
    • 1
    • 2
  • Rebecca Fahrig
    • 4
  • Joachim Hornegger
    • 1
    • 2
  1. 1.Pattern Recognition Lab., Computer Science DepartmentFriedrich-Alexander-University Erlangen-NurembergErlangenGermany
  2. 2.Graduate School in Advanced Optical Technologies (SAOT)ErlangenGermany
  3. 3.Siemens AG, HealthcareForchheimGermany
  4. 4.Radiological Sciences LaboratoryStanford UniversityStanfordUSA

Personalised recommendations