Abstract
In this paper, we present an approach for modelling and predicting organ motion from partial information. We used 4D-MRI sequences of 12 subjects to build a statistical population model for respiratory motion of the liver. Using a Bayesian reconstruction approach, a pre-operative CT scan and a few known surrogate markers, we are able to accurately predict the position of the entire liver at all times. The surrogates may, for example, come from ultrasound, portal images captured during radiotherapy or from implanted electromagnetic beacons. In leave-one-out experiments, we achieve an average prediction error of 1.2 mm over sequences of 20 min with only three surrogates. Our model is accurate enough for clinically relevant treatment intervals and has the potential to be used for adapting the gating window in tumour therapy or even for tracking a tumour continuously during irradiation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM T. Math. Software 22(4), 469–483 (1996)
Blanz, V., Vetter, T.: Reconstructing the complete 3D shape of faces from partial information. Informationstechnik und Technische Informatik 44(6), 295–302 (2002)
Ehrhardt, J., Werner, R., Schmidt-Richberg, A., Handels, H.: Statistical modeling of 4d respiratory lung motion using diffeomorphic image registration. IEEE Transactions on Medical Imaging 30(2), 251–265 (2011)
He, T., Xue, Z., Xie, W., Wong, S.T.C.: Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010 Part III. LNCS, vol. 6363, pp. 392–399. Springer, Heidelberg (2010)
Rohlfing, T., Maurer Jr., C.R., O’Dell, W.G., Zhong, J.: Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. Med. Phys. 31(3), 427–432 (2004)
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 T. Med. Imag. 18(8), 712–721 (1999)
Shirato, H., Seppenwoolde, Y., Kitamura, K., Onimura, R., Shimizu, S.: Intrafractional tumor motion: lung and liver. Semin. Radiat. Oncol. 14(1), 10–18 (2004)
von Siebenthal, M., Cattin, P.C., Gamper, U., Lomax, A., Székely, G.: 4D MR Imaging Using Internal Respiratory Gating. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 336–343. Springer, Heidelberg (2005)
von Siebenthal, M., Székely, G., Lomax, A., Cattin, P.: Inter-Subject Modelling of Liver Deformation During Radiation Therapy. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 659–666. Springer, Heidelberg (2007)
von Siebenthal, M., Székely, G., Lomax, A., Cattin, P.: Systematic errors in respiratory gating due to intrafraction deformations of the liver. Med. Phys. 34(9), 3620–3629 (2007)
Zsemlye, G.: Shape Prediction from Partial Information. Ph.D. thesis, ETH Zurich (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Preiswerk, F., Arnold, P., Fasel, B., Cattin, P.C. (2012). A Bayesian Framework for Estimating Respiratory Liver Motion from Sparse Measurements. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-28557-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28556-1
Online ISBN: 978-3-642-28557-8
eBook Packages: Computer ScienceComputer Science (R0)