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MR-CT Image Registration in Liver Cancer Treatment with an Open Configuration MR Scanner

  • Songyuan Tang
  • Yen-wei Chen
  • Rui Xu
  • Yongtian Wang
  • Shigehiro Morikawa
  • Yoshimasa Kurumi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)

Abstract

MR – CT image registration has been used in the liver cancer treatment with an open MR Scanner to guide percutaneous puncture for ablation of tumors. Due to low magnetic field and limited acquisition time, MR images do not always show the target clearly. Sometimes, assistance of CT images is helpful for the navigation to the target. The shape of the liver within the surgical procedure is different from that of preoperative CT images due to the patient position for the convenience of surgery. It is quite difficult to match the images accuracy during surgery. In this paper, we have proposed a method to improve the registration accuracy of images with an open MR scanner and preoperative CT images of the liver. The method includes three parts. Firstly a semiautomatic method is used to extract the liver from MR and CT images as region of interest (ROI). Then, an affine registration is used to match the images roughly. Finally, BSpline-based nonrigid registration is applied. The results are found to be satisfactory with visual inspection by experts and with evaluation by the distance of two liver surfaces, while comparing with other methods.

Keywords

Image Registration Liver Surface Nonrigid Registration Rigid Registration Affine Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Morikawa, S., Inubushi, T., Kurumi, Y., Naka, S., Sato, K., Tani, T., Yamamoto, I., Fujimura, M.: MR-Guided microwave thermocoagulation therapy of liver tumors: initial clinical experiences using a 0.5 T open MR system. J. Magn. Reson Imaging 16, 576–583 (2002)CrossRefGoogle Scholar
  2. 2.
    Morikawa, S., Inubushi, T., Kurumi, Y., Naka, S., Sato, K., Demura, K., Tani, T., Haque, H.A., Tokuda, J., Hata, N.: Advanced computer assistance for magnetic resonance-guided microwave thermocoagulation of liver tumors. Acad. Radiol. 10, 1442–1449 (2003)CrossRefGoogle Scholar
  3. 3.
    Sato, K., Morikawa, S., Inubushi, T., Kurumi, Y., Naka, S., Haque, H.A., Demura, K., Tnai, T.: Alternate biplanar MR navigation for microwave ablation of liver tumors. Magn. Reson Med. Sci. 4, 89–94 (2005)CrossRefGoogle Scholar
  4. 4.
    Venot, A., Golmard, J.L., Lebruchec, J.F., Pronzato, L., Walter, E., Frij, G., Roucayrol, J.C.: Digital methods for change detection in medical images. In: Deconinck, F. (ed.) Information processing in medical imaging, pp. 1–16. Nijhoff publ., Dordrecht (1983)Google Scholar
  5. 5.
    Hoh, C.K., Dahlbom, M., Harris, G., Choi, Y., Hawkins, R.A., Phelps, M.E., Maddahi, J.: Automated iterative three-dimensional registration of positron emission tomography images. Journal of Nuclear Medicine 34, 2009–2018 (1993)Google Scholar
  6. 6.
    Scott, A.M., Macapinlac, H., Divgi, C.R., Zhang, J.J., Kalaigian, H., Pentlow, K., Hilton, S., Graham, M.C., Sgouros, G., Pelizzari, C., Chen, G., Schlom, J., Goldsmith, S.J., Larson, S.M.: Clinical validation of SPECT and CT/MRI image registration in radiolabeled monoclonal antibody studies of colorectal carcinoma. Journal of Nuclear Medicine 35, 1976–1984 (1994)Google Scholar
  7. 7.
    Scott, A.M., Macapinlac, H., Zhang, J., Daghighian, F., Montemayor, N., Kalaigian, H., Sgouros, G., Graham, M.C., Kolbert, K., Yeh, S.D.J., Lai, E., Goldsmith, S.J., Larson, S.M.: Image registration of SPECT and CT images using an external fiduciary band and three-dimensional surface fitting in metastatic thyroid cancer. Journal of Nuclear Medicine 36, 100–103 (1995)Google Scholar
  8. 8.
    Mehrdad, S., et al.: Automatic CT–SPECT registration of livers treated with radioactive microspheres. Phys. Med. Biol. 49 (2004)Google Scholar
  9. 9.
    Andres, C., Jeffrey, L.D., Jonathan, S.L., David, L.W.: Semiautomatic 3-D Image Registration as Applied to Interventional MRI Liver Cancer Treatment. IEEE Transaction on Medical Imaging 19, 175–185 (2000)CrossRefGoogle Scholar
  10. 10.
    Pluim, J.: Information Based Registration of Medical Images, PhD thesis (2003)Google Scholar
  11. 11.
    Kelmen, A., Szekely, G., Gerig, G.: Elastic Model-Based Segmentation of 3-D Neuroradiological Data Sets. IEEE Transaction on Medical Imaging 18(10), 828–839 (1999)CrossRefGoogle Scholar
  12. 12.
    Smith, S.M.: Fast Robust Automated Brain Extraction. Human Brain Mapping 17, 143–155 (2002)CrossRefGoogle Scholar
  13. 13.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: f-Information measures in medical image registration. IEEE Transactions on Medical Imaging 23, 1506–1518 (2004)CrossRefGoogle Scholar
  14. 14.
    Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modelal volume registration by maximization of mutual information. Medical Image Analysis 1, 35–51 (1996)CrossRefGoogle Scholar
  15. 15.
    William, H., Saul, A.T., et al.: http://www.library.cornell.edu/nr/bookcpdf.html
  16. 16.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transaction on Medical Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  17. 17.
    Hajnal, J.V., Hill, D.L.G., Hawkes, D.J.: Medical Image Registration. CRC Press, Boca Raton (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Songyuan Tang
    • 1
    • 2
  • Yen-wei Chen
    • 1
  • Rui Xu
    • 1
  • Yongtian Wang
    • 2
  • Shigehiro Morikawa
    • 3
  • Yoshimasa Kurumi
    • 3
  1. 1.College of Information Science and EngineeringRitsumeikan UniversityJapan
  2. 2.Department of Opto-electronic EngineeringBeijing Institute of TechnologyP.R. China
  3. 3.Shiga University of Medical ScienceJapan

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