Skip to main content

Advertisement

Log in

Registration by interactive inverse simulation: application for adaptive radiotherapy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This paper introduces a new methodology for semi-automatic registration of anatomical structure deformations. The contribution is to use an interactive inverse simulation of physics-based deformable model, computed in real time.

Methods

The method relies on nonlinear finite element method (FEM) within a constraint-based framework. Given a set of few registered points provided by the user, a real-time optimization adapts the boundary conditions and(/or) some parameters of the FEM in order to obtain the adequate geometrical deformations. To dramatically fasten the process, the method relies on a projection of the model in the space of the optimization variables. In this reduced space, a quadratic programming problem is formulated and solved very quickly. The method is validated with numerical examples for retrieving some unknown parameters such as the Young’s modulus and some pressures on the boundaries of the model.

Results

The approach is employed in the context of radiotherapy of the neck where weight loss during the 7 weeks of the therapy modifies the volume of the anatomical structures and induces large deformations. Indeed, sensitive structures such as the parotid glands may cross the target volume due to these deformations which leads to adverse effects for the patient. We thus apply the approach for the registration of the parotid glands during the radiotherapy of the head and neck cancer.

Conclusions

The results show how the method could be used in a clinical routine and be employed in the planning in order to limit the radiations of these glands.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. We emphasize that these points can be interpolated between the nodes of the mesh using the FEM shape functions. In that case, the value of the shape function will be used to fill the rows of matrix \(\mathbf {J}\) corresponding to the Lagrange multipliers.

References

  1. Webb S (2003) The physical basis of IMRT and inverse planning. Br J Radiol 76:678–689

    Article  CAS  PubMed  Google Scholar 

  2. Goitein M, Abrams M (1983) Multi-dimensional treatment planning: I. Delineation of anatomy. Int J Radiat Oncol Biol Phys 9(6):777–787

    Article  CAS  PubMed  Google Scholar 

  3. Nelms B, Tomé W, Robinson G, Wheller J (2012) Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer. Int J Radiat Oncol Biol Phys 82:368–378

    Article  PubMed  Google Scholar 

  4. Veiga C, McClelland J, Moinuddin S, Lourenço A, Ricketts K, Annkah J, Modat M, Ourselin S, D’Souza D, Royle G (2014) Toward adaptive radiotherapy for head and neck patients: feasibility study on using CT-to-CBCT deformable registration for dose of the day calculations. Med Phys 41(3):031703

    Article  PubMed  Google Scholar 

  5. Dirix P, Nuyts S, Van den Bogaert W (2006) Radiation-induced xerostomia in patients with head and neck cancer. Cancer 107(11):2525–2534

    Article  PubMed  Google Scholar 

  6. Castadot P, Lee JA, Parraga A, Geets X, Macq B, Grégoire V (2008) Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. Radiother Oncol 89(1):1–12

    Article  PubMed  Google Scholar 

  7. Crum WR, Hartkens T, Hill DLG (2004) Non-rigid image registration: theory and practice. Br J Radiol 77:S140–S153

  8. Das IJ, Moskvin V, Johnstone PA (2009) Analysis of treatment planning time among systems and planners for intensity-modulated radiation therapy. J Am Coll Radiol 6(7):514–517

    Article  PubMed  Google Scholar 

  9. Schnabel J, Tanner C, Castellano-Smith A, Degenhard A, Leach M, Hose DR, Hawkes D (2003) Validation of nonrigid image registration using finite-element methods: application to breast MR images. IEEE Trans Med Imaging 22(2):238–247

    Article  PubMed  Google Scholar 

  10. Zhu Y, Hall TJ, Jiang J (2003) A finite-element approach for Young’s modulus reconstruction. IEEE Trans Med Imaging 22(7):890–901

    Article  PubMed  Google Scholar 

  11. Paragios N, Rousson M, Ramesh V (2003) Non-rigid registration using distance functions. Comput Vis Image Underst 89(1):142–165

    Article  Google Scholar 

  12. Kass M, Witkin A, Terzopoulos D (1987) Snake: active contour model, volume 1

  13. Barker JL, Garden AS, Ang KK, O’Daniel JC, Wang H, Court LE, Morrison WH, Rosenthal DI, Chao KS, Tucker SL, Mohan R, Dong L (2004) Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys 59(4):960–970

  14. Becker M, Teschner M (2007) Robust and efficient estimation of elasticity parameters using the linear finite element method. In: SimVis, pp 15–28

  15. Eskandari H, Salcudean S, Rohling R, Bell I (2011) Real-time solution of the finite element inverse problem of viscoelasticity. Inverse Probl J 27(8):85–102

    Google Scholar 

  16. Lee H-P, Foskey M, Niethammer M, Krajcevski P, Lin M (2012) Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis. IEEE Trans Med Imaging 31:2156–2168

  17. Faure F, Duriez C, Delingette H, Allard J, Gilles B, Marchesseau S, Talbot H, Courtecuisse H, Bousquet G, Peterlik I, Cotin S (2012) Sofa: a multi-model framework for interactive physical simulation. In: The book “Soft tissue biomechanical modeling for computer assisted surgery”, pp 283–321

  18. Baraff D, Witkin A (1998) Large steps in cloth simulation. In: SIGGRAPH ’98, pp 43–54

  19. Guebert C, Duriez C, Grisoni L (2008) Unified processing of constraints for interactive simulation. In: VRIPHYS

  20. Fogel E, Teillaud M (2014) The computational geometry algorithms library CGAL. ACM Commun Comput Algebra 47(3/4):85–87

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to thank Pierre Jannin for his advice on validation. The project had the financial support of ANR JCJC Simi3 (Ideas), Oscar Lambret Hospital and Inria Lille Nord-Europe research centre.

Conflict of interest

Eulalie Coevoet, Nick Reynaert, Eric Lartigau, Luis Schiappacasse, Jérémie Dequidt and Christian Duriez declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eulalie Coevoet.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coevoet, E., Reynaert, N., Lartigau, E. et al. Registration by interactive inverse simulation: application for adaptive radiotherapy. Int J CARS 10, 1193–1200 (2015). https://doi.org/10.1007/s11548-015-1175-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-015-1175-4

Keywords

Navigation