Abstract
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use the linearity of the X-ray transform to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. The bilinear estimation process is supported by a B-spline interpolation using prior knowledge about the trajectory angle. Consequently, extraction of respiratory features simplifies to a linear problem. Though the need for a prior 4D CT seems steep, our proposed use-case of driving a respiratory motion model in radiation therapy usually meets this requirement. We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and achieve a mean estimation error of \(3.01\%\) in the gray values for unseen viewing angles. We further demonstrate suitability of the extracted weights to drive a motion model for treatments with a continuously rotating gantry.
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
References
Aichert, A., et al.: Epipolar consistency in transmission imaging. IEEE Trans. Med. Imaging 34(11), 2205–2219 (2015). https://doi.org/10.1109/TMI.2015.2426417
Çimen, S., Hoogendoorn, C., Morris, P.D., Gunn, J., Frangi, A.F.: Reconstruction of coronary trees from 3DRA using a 3D+t statistical cardiac prior. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 619–626. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_77
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)
De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)
Fassi, A., et al.: Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy. Phys. Med. Biol. 60(4), 1565–1582 (2015). https://doi.org/10.1088/0031-9155/60/4/1565
Fischer, P., Pohl, T., Faranesh, A., Maier, A., Hornegger, J.: Unsupervised learning for robust respiratory signal estimation from x-ray fluoroscopy. IEEE Trans. Med. Imaging 36(4), 865–877 (2017). https://doi.org/10.1109/TMI.2016.2609888
Geimer, T., Unberath, M., Birlutiu, A., Wölfelschneider, J., Bert, C., Maier, A.: A kernel-based framework for intra-fractional respiratory motion estimation in radiation therapy. In: Proceedings of IEEE International Symposium on Biomed Imaging, pp. 1036–1039 (2017)
Keall, P.J., et al.: The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med. Phys. 33(10), 3874–3900 (2006). https://doi.org/10.1118/1.2349696
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009). https://doi.org/10.1137/07070111X
Maier, A., et al.: CONRAD-a software framework for cone-beam imaging in radiology. Med. Phys. 40(11), 111914 (2013). https://doi.org/10.1118/1.4824926
McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: a review. Med. Image Anal. 17(1), 19–42 (2013). https://doi.org/10.1016/j.media.2012.09.005
Prasetio, H., Wölfelschneider, J., Ziegler, M., Serpa, M., Witulla, B., Bert, C.: Dose calculation and verification of the vero gimbal tracking treatment delivery. Phys. Med. Biol. 63(3), 035043 (2018). https://doi.org/10.1088/1361-6560/aaa617
Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12(6), 1247–1283 (2000)
Wilms, M., Werner, R., Ehrhardt, J., Schmidt-Richberg, A., Schlemmer, H.P., Handels, H.: Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy. Phys. Med. Biol. 59(5), 1147–1164 (2014). https://doi.org/10.1088/0031-9155/59/5/1147
Yan, H., et al.: Extracting respiratory signals from thoracic cone beam CT projections. Phys. Med. Biol. 58(5), 1447–64 (2013). https://doi.org/10.1088/0031-9155/58/5/1447
Acknowledgement
This work was partially conducted at the ACRF Image X Institute as part of a visiting research scholar program. The authors gratefully acknowledge funding of this research stay by the Erlangen Graduate School in Advanced Optical Technologies (SAOT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Geimer, T. et al. (2019). Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-12939-2_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12938-5
Online ISBN: 978-3-030-12939-2
eBook Packages: Computer ScienceComputer Science (R0)