Image Registration for Interventional MRI Guided Procedures: Interpolation Methods, Similarity Measurements, and Applications to the Prostate
Nuclear medicine can detect and localize tumor in the prostate not reliably seen in MR. We are investigating methods to combine the advantages of SPECT with interventional MRI (iMRI) guided radiofrequency thermal ablation of the prostate. Our approach is to first register the low-resolution functional images with a high resolution MR volume. Then, by combining the high-resolution MR image with live-time iMRI acquisitions, we can, in turn, include the functional data and high-resolution anatomic information into the iMRI system for improved tumor targeting. In this study, we investigated registration methods for combining noisy, thick iMRI image slices with high-resolution MR volumes. We compared three similarity measures, i.e., normalized mutual information, mutual information, and correlation coefficient; and three interpolation methods, i.e., re-normalized sinc, tri-linear, and nearest neighbor. Registration experiments showed that transverse slice images covering the prostate work best with a registration error of ≈ 0.5 mm as compared to our volume-to-volume registration that was previously shown to be quite accurate for these image pairs.
KeywordsMutual Information Interpolation Method Image Registration Normalize Mutual Information Registration Error
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- 2.Lee, Z., Sodee, D.B., Duerk, J.L., Nelson, A.D., Berridge, M.S.: Automatic registration of SPECT-MRI in the pelvis. Journal of Nuclear Medicine 41, 232 (2000)Google Scholar
- 4.Fei, B.W., Duerk, J.L., Boll, D.T., Lewin, J.S., Wilson, D.L.: Slice to volume registration and its potential application tointerventional MRI guided radiofrequency thermal ablation of prostate cancer. IEEE Transactions on Medical Imaging 22 (2003)Google Scholar
- 7.Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multimodality image registration using information theory. In: Information Processing in Medical Imaging: Proc. 14th International Conference IPMI 1995. Computational Imaging and Vision, pp. 287–298 (1995)Google Scholar
- 9.Press, W.H., Teukolsky, S.A., Vellerling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. The Press Syndicate of the Cambridge University, New York (1992)Google Scholar
- 14.Gregg, R.C., Nowak, R.D.: Noise removal methods for high resolution MRI. IEEE Nuclear Science Symposium 2, 1117–1121 (1997)Google Scholar