Abstract
Sensorless freehand 3D ultrasound (US) uses speckle decorrelation to estimate small rigid motions between pairs of 2D images. Trajectory estimation combines these motion estimates to obtain the position each image relative to the first. This is prone to the accumulation of measurement bias. Whereas previous work concentrated on correcting biases at the source, this paper proposes to reduce error accumulation by carefully choosing the set of measurements used for trajectory estimation. An undirected graph is created with frames as vertices and motion measurements as edges. Using constrained shortest paths in the graph, random trajectories are generated and averaged to obtain trajectory estimate and uncertainty. To improve accuracy, a Gaussian process regressor is trained on tracked US sequences to predict systematic motion measurement error, which is then used to weigh the edges of the graph. Results on speckle phantom imagery show significantly improved trajectory estimates in comparison with the state-of-the-art, promising accurate volumetric reconstruction.
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References
Afsham, N., et al.: A generalized correlation-based model for out-of-plane motion estimation in freehand ultrasound. IEEE Trans. Med. Imaging 33(1), 186–199 (2014)
Afsham, N., et al.: Nonlocal means filter-based speckle tracking. IEEE Trans. Ultrason. Ferroelectr. 62(8), 1501–1515 (2015)
Chen, J.F., et al.: Determination of scan-plane motion using speckle decorrelation: theoretical considerations and initial test. Int. J. Imaging Syst. Technol. 8(1), 38–44 (1997)
Conrath, J., Laporte, C.: Towards improving the accuracy of sensorless freehand 3D ultrasound by learning. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds.) MLMI 2012. LNCS, vol. 7588, pp. 78–85. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35428-1_10
van De Kraats, E.: Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans. Med. Imaging 24(9), 1177–1189 (2005)
Gee, A.H., et al.: Sensorless freehand 3D ultrasound in real tissue: speckle decorrelation without fully developed speckle. Med. Image Anal. 10(2), 137–149 (2006)
Govindu, V.M.: Lie-algebraic averaging for globally consistent motion estimation. In: Proceedings of CVPR, vol. 1, pp. 684–691 (2004)
Housden, R.J., et al.: Sensorless reconstruction of unconstrained freehand 3D ultrasound data. Ultrasound Med. Biol. 33(3), 408–419 (2007)
Housden, R., et al.: Rotational motion in sensorless freehand three-dimensional ultrasound. Ultrasonics 48(5), 412–422 (2008)
Laporte, C., Arbel, T.: Combinatorial and probabilistic fusion of noisy correlation measurements for untracked freehand 3-D ultrasound. IEEE Trans. Med. Imaging 27(7), 984–994 (2008)
Laporte, C., Arbel, T.: Measurement selection in untracked freehand 3D ultrasound. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 127–134. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15705-9_16
Laporte, C., Arbel, T.: Learning to estimate out-of-plane motion in ultrasound imagery of real tissue. Med. Image Anal. 15(2), 202–213 (2011)
Li, P.C., et al.: Tissue motion and elevational speckle decorrelation in freehand 3D ultrasound. Ultrason. Imaging 24(1), 1–12 (2002)
Rasmussen, C.E.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Rousseau, F., et al.: Confhusius: a robust and fully automatic calibration method for 3D freehand ultrasound. Med. Image Anal. 9, 25–38 (2005)
Rousseau, F., et al.: A novel temporal calibration method for 3D ultrasound. IEEE Trans. Med. Imaging 25(8), 1108–1112 (2006)
Tuthill, T.A., et al.: Automated three-dimensional US frame positioning computed from elevational speckle decorrelation. Radiology 209(2), 575–582 (1998)
Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. 13(4), 376–380 (1991)
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Tetrel, L., Chebrek, H., Laporte, C. (2016). Learning for Graph-Based Sensorless Freehand 3D Ultrasound. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_25
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DOI: https://doi.org/10.1007/978-3-319-47157-0_25
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