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Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

  • Alina GigerEmail author
  • Christoph Jud
  • Damien Nguyen
  • Miriam Krieger
  • Ye Zhang
  • Antony J. Lomax
  • Oliver Bieri
  • Rares Salomir
  • Philippe C. Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11843)

Abstract

Motion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for inter-fractional motion management in radiotherapy.

Keywords

Motion prediction Ultrasound 4D MRI Radiotherapy 

Notes

Acknowledgement

We thank Pauline Guillemin from the University of Geneva, Switzerland, for her indispensable support with the data acquisition. This work was supported by the Swiss National Science Foundation, SNSF (320030_163330/1) and the GPU Grant Program of NVIDIA (Nvidia Corporation, Santa Clara, California, USA).

References

  1. 1.
    Bert, C., Durante, M.: Motion in radiotherapy: particle therapy. Phys. Med. Biol. 56(16), R113 (2011)CrossRefGoogle Scholar
  2. 2.
    Giger, A., et al.: Respiratory motion modelling using cGANs. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 81–88. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00937-3_10CrossRefGoogle Scholar
  3. 3.
    Jud, C., Giger, A., Sandkühler, R., Cattin, P.C.: A localized statistical motion model as a reproducing kernel for non-rigid image registration. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 261–269. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_30CrossRefGoogle Scholar
  4. 4.
    Jud, C., Nguyen, D., Sandkühler, R., Giger, A., Bieri, O., Cattin, P.C.: Motion aware MR imaging via spatial core correspondence. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 198–205. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00928-1_23CrossRefGoogle Scholar
  5. 5.
    Krieger, M., Giger, A., Weber, D., Lomax, A., Zhang, Y.: PV-0422 consequences of respiratory motion variability in lung 4DMRI datasets. Radiother. Oncol. 133, S219–S220 (2019)CrossRefGoogle Scholar
  6. 6.
    McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: a review. Med. Image Anal. 17(1), 19–42 (2013)CrossRefGoogle Scholar
  7. 7.
    Mostafaei, F., et al.: Feasibility of real-time lung tumor motion monitoring using intrafractional ultrasound and kV cone beam projection images. Med. Phys. 45(10), 4619–4626 (2018)CrossRefGoogle Scholar
  8. 8.
    O’Shea, T., Bamber, J., Fontanarosa, D., van der Meer, S., Verhaegen, F., Harris, E.: Review of ultrasound image guidance in external beam radiotherapy part II: intra-fraction motion management and novel applications. Phys. Med. Biol. 61(8), R90 (2016)CrossRefGoogle Scholar
  9. 9.
    Preiswerk, F., et al.: Model-guided respiratory organ motion prediction of the liver from 2D ultrasound. Med. Image Anal. 18(5), 740–751 (2014)CrossRefGoogle Scholar
  10. 10.
    Sandkühler, R., Jud, C., Andermatt, S., Cattin, P.C.: Airlab: autograd image registration laboratory. arXiv preprint arXiv:1806.09907 (2018)
  11. 11.
    von Siebenthal, M., Szekely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52(6), 1547 (2007)CrossRefGoogle Scholar
  12. 12.
    Stemkens, B., Paulson, E.S., Tijssen, R.H.: Nuts and bolts of 4D-MRI for radiotherapy. Phys. Med. Biol. 63(21), 21TR01 (2018)CrossRefGoogle Scholar
  13. 13.
    Trnková, P., et al.: Clinical implementations of 4D pencil beam scanned particle therapy: report on the 4D treatment planning workshop 2016 and 2017. Physica Med. 54, 121–130 (2018)CrossRefGoogle Scholar
  14. 14.
    Vezhnevets, V., Konouchine, V.: GrowCut: interactive multi-label ND image segmentation by cellular automata. In: Proceddings of Graphicon, vol. 1, pp. 150–156. Citeseer (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for medical Image Analysis and NavigationUniversity of BaselAllschwilSwitzerland
  2. 2.Department of Biomedical EngineeringUniversity of BaselAllschwilSwitzerland
  3. 3.Department of Radiology, Division of Radiological PhysicsUniversity Hospital BaselBaselSwitzerland
  4. 4.Center for Proton Therapy, Paul Scherrer Institute (PSI)Villigen PSISwitzerland
  5. 5.Department of PhysicsETH ZurichZurichSwitzerland
  6. 6.Image Guided Interventions LaboratoryUniversity of GenevaGenevaSwitzerland

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