A Continuity Equation Based Optical Flow Method for Cardiac Motion Correction in 3D PET Data

  • Mohammad Dawood
  • Christoph Brune
  • Xiaoyi Jiang
  • Florian Büther
  • Martin Burger
  • Otmar Schober
  • Michael Schäfers
  • Klaus P Schäfers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)

Abstract

Cardiac Motion artifacts in PET are a well known problem. The heart undergoes two types of motion, the motion due to respiratory displacement and the motion due to cardiac contraction. These movements lead to blurring of data and to inaccuracies in the quantification. In this study a continuity equation based optical flow method is presented and results on 3D PET patient datasets for cardiac motion correction are presented. The method was evaluated with respect to three criteria: correlation between the images, myocardial thickness and the blood pool activity curves. The results showed that the method was successful in motion correcting the data with high precision.

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References

  1. 1.
    Vandenberghe, S., D’Asseler, Y., Van de Walle, R., Kauppinen, T., Koole, M., Bouwens, L., Van Laere, K., Lemahieu, I., Dierckx, R.A.: Iterative reconstruction algorithms in nuclear medicine. Comput. Med. Imaging Graph. 25(2), 105–111 (2001)CrossRefGoogle Scholar
  2. 2.
    Erdi, Y.E., Nehmeh, S.A., Pan, T., Pevsner, A., Rosenzweig, K.E., Mageras, G., Yorke, E.D., Schoder, H., Hsiao, W., Squire, O.D., Vernon, P., Ashman, J.B., Mostafavi, H., Larson, S.M., Humm, J.L.: The CT motion quantitation of lung lesions and its impact on PET-measured SUVs. J. Nucl. Med. 45(8), 1287–1292 (2004)Google Scholar
  3. 3.
    Shepp, L.A., Vardi, Y.: Maximum Likelihood Reconstruction for Emission Tomography. IEEE Trans. Med. Imag. 1(2), 113–122 (1982)CrossRefGoogle Scholar
  4. 4.
    Osman, M.M., Cohade, C., Nakamoto, Y., Marshall, L.T., Leal, J.P., Wahl, R.L.: Clinically significant inaccurate localization of lesions with PET/CT: frequency in 300 patients. J. Nucl. Med. 44(2), 240–243 (2003)Google Scholar
  5. 5.
    Nakamoto, Y., Chin, B.B., Cohade, C., Osman, M., Tatsumi, M., Wahl, R.L.: PET/CT: artifacts caused by bowel motion. Nucl. Med. Commun. 25(3), 221–225 (2004)CrossRefGoogle Scholar
  6. 6.
    Dawood, M., Büther, F., Lang, N., Schober, O., Schäfers, K.P.: Respiratory gating in positron emission tomography: A quantitative comparision of different gating schemes. Medical Physics 34, 3067–3076 (2007)CrossRefGoogle Scholar
  7. 7.
    Qiao, F., Pan, T., Clark, J.W., Mawlawi, O.R.: A motion-incorporated reconstruction method for gated PET studies. Phys. Med. Biol. 51(15), 3769–3783 (2006)CrossRefGoogle Scholar
  8. 8.
    Mair, B.A., Gilland, D.R., Sun, J.: Estimation of images and nonrigid deformations in gated emission CT. IEEE Trans. Med. Imaging 25(9), 1130–1144 (2006)CrossRefGoogle Scholar
  9. 9.
    Lamare, F., Carbayo, M.J.L., Cresson, T., Kontaxakis, G., Santos, A., Rest, C.C.L., Reader, A.J., Visvikis, D.: List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations. Phys. Med. Biol. 52, 5187–5204 (2007)CrossRefGoogle Scholar
  10. 10.
    Klein, G.J., Reutter, B.W., Huesman, R.H.: Non-rigid summing of gated PET via optical flow. IEEE Transactions on Nuclear Science 44(4), 1509–1512 (1997)CrossRefGoogle Scholar
  11. 11.
    Gilland, D.R., Mair, B.A., Bowsher, J.E., Jaszczak, R.J.: Simultaneous reconstruction and motion estimation for gated cardiac ECT. IEEE Transactions on Nuclear Science 49(5), 2344–2349 (2002)CrossRefGoogle Scholar
  12. 12.
    Dawood, M., Büther, F., Jiang, X., Schäfers, K.P.: Respiratory Motion Correction in 3D PET Data with Advanced Optical Flow Algorithms. IEEE Trans. Med. Imaging 27(8), 1164–1175 (2008)CrossRefGoogle Scholar
  13. 13.
    Corpetti, T., Heitz, D., Arroyo, G., Mémin, E., Santa-Cruz, A.: Fluid experimental flow estimation based on an optical-flow scheme. Experiments in Fluids 40, 80–97 (2006)CrossRefGoogle Scholar
  14. 14.
    Segars, W.P.: Development and application of the new dynamic NURBS-based cardiac-torso (NCAT) phantom, in Biomedical Engineering. Dissertation, University of North Carolina (2001)Google Scholar
  15. 15.
    Dawood, M., Lang, N., Jiang, X., Schäfers, K.P.: Lung motion correction on respiratory gated 3-D PET/CT images. IEEE Trans. Med. Imaging 25(4), 476–485 (2006)CrossRefGoogle Scholar
  16. 16.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61(3), 211–231 (2005)CrossRefGoogle Scholar
  17. 17.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  18. 18.
    Vitale, G.D., de Kemp, R.A., Ruddy, T.D., Williams, K., Beanlands, R.S.: Myocardial glucose utilization and optimization of (18)F-FDG PET imaging in patients with non-insulin-dependent diabetes mellitus, coronary artery disease, and left ventricular dysfunction. J. Nucl. Med. 42(12), 1730–1736 (2001)Google Scholar
  19. 19.
    Erdi, Y.E., Nehmeh, S.A., Mulnix, T., Humm, J.L., Watson, C.C.: PET performance measurements for an LSO-based combined PET/CT scanner using the national electrical manufacturers association nu 2-2001 standard. J. Nucl. Med. 45(5), 813–821 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohammad Dawood
    • 1
    • 2
  • Christoph Brune
    • 2
  • Xiaoyi Jiang
    • 2
  • Florian Büther
    • 1
  • Martin Burger
    • 2
  • Otmar Schober
    • 3
  • Michael Schäfers
    • 1
    • 3
  • Klaus P Schäfers
    • 1
  1. 1.European Institute for Molecular ImagingUniversity of MünsterMünsterGermany
  2. 2.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany
  3. 3.Department of Nuclear MedicineUniversity Hospital MünsterMünsterGermay

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