FIMH 2009: Functional Imaging and Modeling of the Heart pp 427-436 | Cite as
Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates
Conference paper
Abstract
Automated motion tracking of the myocardium from 3D echocardiography provides insight into heart’s architecture and function. We present a method for 3D cardiac motion tracking using non-rigid image registration. Our contribution is two-fold. We introduce a new similarity measure derived from a maximum likelihood perspective taking into account physical properties of ultrasound image acquisition and formation. Second, we use envelope-detected 3D echo images in the raw spherical coordinates format, which preserves speckle statistics and represents a compromise between signal detail and data complexity. We derive mechanical measures such as strain and twist, and validate using sonomicrometry in open-chest piglets. The results demonstrate the accuracy and feasibility of our method for studying cardiac motion.
Keywords
Similarity Measure Ultrasound Image Speckle Noise Radio Frequency Signal Nakagami Distribution
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.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Lang, M., Mor-Avi, V., Sugeng, L., Nieman, P., Sahn, D.J.: Three-dimensional echocardiography. Jour. of the Am. Coll. of Cardiology 48(10), 2053–2069 (2006)CrossRefGoogle Scholar
- 2.Myronenko, A., Song, X., Sahn, D.J.: LV motion tracking from 3D echocardiography using textural and structural information. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 428–435. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 3.Elen, A., Choi, H.F., Loeckx, D., Gao, H., Claus, P., Suetens, P., Maes, F., D’hooge, J.: Three-dimensional cardiac strain estimation using spatio–temporal elastic registration of ultrasound images: A feasibility study. TMI 27(11), 1580–1591 (2008)Google Scholar
- 4.Leung, K., van Stralen, M., Nemes, A., Voormolen, M., Voormolen, M., van Burken, G., Geleijnse, M.J.t., Reiber, J., de Jong, N., van der Steen, A., Bosch, J.: Sparse registration for 3D stress echocardiography. Medical Imaging 27(11), 1568–1579 (2008)CrossRefGoogle Scholar
- 5.Ledesma-Carbayo, M., Kybic, J., Desco, M., Santos, A., Sühling, M., Hunziker, P., Unser, M.: Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation. IEEE Transactions on Medical Imaging 24(9), 1113–1126 (2005)CrossRefGoogle Scholar
- 6.Sanchez-Ortiz, G., Wright, G., Clarke, N., Declerck, J., Banning, A., Noble, J.: Automated 3-D echocardiography analysis compared with manual delineations and spect muga. IEEE Transactions on Medical Imaging 21(9), 1069–1076 (2002)CrossRefGoogle Scholar
- 7.Cohen, B., Dinstein, I.: New maximum likelihood motion estimation schemes for noisy ultrasound images. Pattern Recognition 35(2), 455–463 (2002)CrossRefMATHGoogle Scholar
- 8.Cobbold, R.S.C.: Foundations of Biomedical Ultrasound. Biomedical Engineering Series. Oxford University Press, Oxford (2006)Google Scholar
- 9.Burckhardt, C.B.: Speckle in ultrasound b-mode scans. IEEE Trans. on Sonics and Ultrasonics 25(1) (1978)Google Scholar
- 10.Meunier, J.: Tissue motion assessment from 3D echographic speckle tracking. Physics in medicine and biology 43(5), 1241–1254 (1998)CrossRefGoogle Scholar
- 11.Goodman, J.W.: Speckle Phenomena in Optics. Theory and Applications (2006)Google Scholar
- 12.Chandrashekara, R., Mohiaddin, R.H., Rueckert, D.: Daniel rueckert. In: Ayache, N., Delingette, H. (eds.) IS4TM 2003. LNCS, vol. 2673, pp. 88–99. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 13.Roche, A., Malandain, G., Ayache, N.: Unifying maximum likelihood approaches in medical image registration. IJIST 11, 71–80 (2000)Google Scholar
- 14.Papoulis, A.: Probability, Random Variables, and Stochastic Processes (1991)Google Scholar
- 15.Boukerroui, D., Noble, J.A., Brady, M.: Velocity estimation in ultrasound images: A block matching approach. IPMI 2732, 586–598 (2003)Google Scholar
- 16.Revell, J., Mirmehdi, M., McNally, D.: Combined ultrasound speckle pattern similarity measures. In: MIUA, pp. 149–153. BMVA Press (2004)Google Scholar
- 17.Song, X., Myronenko, A., Sahn, D.J.: Speckle tracking in 3D echocardiography with motion coherence. In: Comp. Vision and Patt. Recognition (CVPR) (2007)Google Scholar
- 18.Linguraru, M.G., Vasilyev, N.V., Marx, G.R., Tworetzky, W., Nido, P.J.D., Howe, R.D.: Fast block flow tracking of atrial septal defects in 4D echo. MIA (2008)Google Scholar
- 19.Sloane, N.: Online enc. of integer sequences. A002457, A002802, A020918 (2008)Google Scholar
- 20.Miller, K., Bernstein, R., Bluemenson, L.: Generalized rayleigh processes. Quarterly of Applied Mathematics 16, 137–145 (1958)MathSciNetCrossRefGoogle Scholar
- 21.Shankar, P.M.: A general statistical model for ultrasonic backscattering from tissues. IEEE T. Ultr., Ferr. and Frequency Control 47(3), 727–736 (2000)CrossRefGoogle Scholar
- 22.Tan, C.C., Beaulieu, N.C.: Infinite series representations of the bivariate rayleigh and nakagami-m distributions. IEEE Trans. Communications 45(10) (1997)Google Scholar
- 23.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 Trans. Image Processing 18(8), 712–721 (1999)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2009