Abstract
An iterative approach to building a surrogate-driven motion model exclusively from cone-beam CT projections is presented. At each iteration the motion model is updated via an analytical expression derived from an optical flow-based approach, with corresponding improvements in the motion compensated reconstruction. The differences between the actual and estimated motion, as seen in the projections, are incorporated into a modified CBCT reconstruction. The correlations between these differences and the surrogate signals used in the motion model are also taken into account in determining the motion model updates. The updates are then composed with the previous estimate of the motion model and set as the new estimate of the motion model. New updates to this new estimate can then be calculated.
The motion model could be used to better understand respiratory motion immediately prior to a fraction of radiotherapy treatment, or to monitor key regions of interest during tracked treatments. This method would also be a promising candidate to adapt an older model built during planning to the day of treatment. The local, voxel-wise updates to the model can account for large inter-fraction changes, specific to the day of treatment.
Results on a simulated case are presented, derived from an actual patient dataset undergoing radiotherapy treatment for lung cancer. With the fitted motion, simulated projections of the animated patient volume were seen to be more similar to the actual projections than projections of the static patient volume. When compared with the actual motion, the mean L2-error over the entire patient was reduced to 0.46 mm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Verellen, D., De Riddeer, M., Linthout, N., Tournel, K., Soete, G., Storme, G.: Innovations in image-guided radiotherapy. Nature Review (2007)
Benedict, S.H., Yenice, K.M., Followill, D., Galvin, J.M., Hinson, W., Kavanagh, B., Keall, P., Lovelock, M., Meeks, S., Papiez, L., Purdie, T., Sadagopan, R., Schell, M.C., Salter, B., Schlesinger, D.J., Shiu, A.S., Solberg, T., Song, D.Y., Stieber, V., Timmerman, R., Tomé, W.A., Verellen, D., Wang, L., Yin, F.F.: Stereotactic body radiation therapy: The report of aapm task group 101. Medical Physics 37(8), 4078–4101 (2010)
Kubo, H.D., Hill, B.C.: Respiration gated radiotherapy treatment: a technical study. Physics in Medicine and Biology 41(1), 83 (1996)
Schweikard, A., Shiomi, H., Adler, J.: Respiration tracking in radiosurgery. Medical Physics 31(10), 2738–2741 (2004)
Martin, J., McClelland, J., Thomas, C., Wildermuth, K., Landau, D., Ourselin, S., Hawkes, D.: Motion modelling and motion compensated reconstruction of tumours in cone-beam computed tomography. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), pp. 281–286 (January 2012)
Martin, J., McClelland, J., Yip, C., Thomas, C., Hartill, C., Ahmad, S., O’Brien, R., Meir, I., Landau, D., Hawkes, D.: Building motion models of lung tumours from cone-beam ct for radiotherapy applications. Physics in Medicine and Biology 58(6), 1809 (2013)
Martin, J., McClelland, J., Yip, C., Thomas, C., Hartill, C., Ahmad, S., Meir, I., Landau, D., Hawkes, D.: Fully-deformable patient motion models from cone-beam ct for radiotherapy applications. In: Proceedings of the 17th International Conference on the Use of Computers in Radiation Therapy (ICCR 2013). 2013 Journal of Physics: Conference Series (2013)
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
Feldkamp, L., Davis, L., Kress, J.: Practical cone-beam algorithm. JOSA A 1(6), 612–619 (1984)
Lewis, J.H., Li, R., Jia, X., Watkins, W.T., Lou, Y., Song, W.Y., Jiang, S.B.: Mitigation of motion artifacts in cbct of lung tumors based on tracked tumor motion during cbct acquisition. Physics in Medicine and Biology 56(17), 5485 (2011)
Rit, S., Wolthaus, J.W.H., van Herk, M., Sonke, J.J.: On-the-fly motion-compensated cone-beam ct using an a priori model of the respiratory motion. Medical Physics 36(6), 2283–2296 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Martin, J., McClelland, J., Champion, B., Hawkes, D.J. (2014). Building Surrogate-Driven Motion Models from Cone-Beam CT via Surrogate-Correlated Optical Flow. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham. https://doi.org/10.1007/978-3-319-07521-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-07521-1_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07520-4
Online ISBN: 978-3-319-07521-1
eBook Packages: Computer ScienceComputer Science (R0)