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Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13386)


Deformable image registration (DIR) is a key element in adaptive radiotherapy (AR) to include anatomical modifications in the adaptive planning. In AR, daily 3D images are acquired and DIR can be used for structure propagation and to deform the daily dose to a reference anatomy. Quantifying the uncertainty associated with DIR is essential. Here, a probabilistic unsupervised deep learning method is presented to predict the variance of a given deformable vector field (DVF). It is shown that the proposed method can predict the uncertainty associated with various conventional DIR algorithms for breathing deformation in the lung. In addition, we show that the uncertainty prediction is accurate also for DIR algorithms not used during the training. Finally, we demonstrate how the resulting DVFs can be used to estimate the dosimetric uncertainty arising from dose deformation.


  • Deformable image registration
  • Proton therapy
  • Adaptive planning
  • Uncertainty
  • Deep learning

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  1. Albertini, F., Matter, M., Nenoff, L., Zhang, Y., Lomax, A.: Online daily adaptive proton therapy. Br. J. Radiol. 93(1107), 20190594 (2020)

    CrossRef  Google Scholar 

  2. Amstutz, F., et al.: An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer. Phys. Med. Biol. 66(10), 105007 (2021)

    CrossRef  Google Scholar 

  3. Brock, K.K., McShan, D.L., Ten Haken, R., Hollister, S., Dawson, L., Balter, J.: Inclusion of organ deformation in dose calculations. Med. Phys. 30(3), 290–295 (2003)

    CrossRef  Google Scholar 

  4. Brock, K.K., Mutic, S., McNutt, T.R., Li, H., Kessler, M.L.: Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM radiation therapy committee task group no. 132. Med. Phys. 44(7), e43–e76 (2017)

    Google Scholar 

  5. Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Phys. Med. Biol. 55(1), 305 (2009)

    CrossRef  Google Scholar 

  6. Castillo, R., et al.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849 (2009)

    CrossRef  Google Scholar 

  7. Chetty, I.J., Rosu-Bubulac, M.: Deformable registration for dose accumulation. In: Seminars in Radiation Oncology, vol. 29, pp. 198–208. Elsevier (2019)

    Google Scholar 

  8. Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)

    CrossRef  Google Scholar 

  9. Hansen, L., Heinrich, M.P.: Tackling the problem of large deformations in deep learning based medical image registration using displacement embeddings. arXiv preprint arXiv:2005.13338 (2020)

  10. Heinrich, M.P.: Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 50–58. Springer, Cham (2019).

    CrossRef  Google Scholar 

  11. Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013).

    CrossRef  Google Scholar 

  12. Jaffray, D.A., Lindsay, P.E., Brock, K.K., Deasy, J.O., Tomé, W.A.: Accurate accumulation of dose for improved understanding of radiation effects in normal tissue. Int. J. Radiation Oncol.* Biol.* Phys. 76(3), S135–S139 (2010)

    Google Scholar 

  13. Janssens, G., et al.: Evaluation of nonrigid registration models for interfraction dose accumulation in radiotherapy. Med. Phys. 36(9Part1), 4268–4276 (2009)

    Google Scholar 

  14. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  15. Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. Adv. Neural Inf. Process. Syst. 28 (2015)

    Google Scholar 

  16. Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2009)

    CrossRef  Google Scholar 

  17. Nenoff, L., et al.: Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy. Radiother. Oncol. 147, 178–185 (2020)

    CrossRef  Google Scholar 

  18. Paganelli, C., Meschini, G., Molinelli, S., Riboldi, M., Baroni, G.: Patient-specific validation of deformable image registration in radiation therapy: overview and caveats. Med. Phys. 45(10), e908–e922 (2018)

    CrossRef  Google Scholar 

  19. Paganetti, H.: Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys. Med. Biol. 57(11), R99 (2012)

    CrossRef  Google Scholar 

  20. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026–8037 (2019)

    Google Scholar 

  21. Rühaak, J.: Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans. Med. Imaging 36(8), 1746–1757 (2017)

    CrossRef  Google Scholar 

  22. Schultheiss, T.E., Tomé, W.A., Orton, C.G.: It is not appropriate to “deform’’ dose along with deformable image registration in adaptive radiotherapy. Med. Phys. 39(11), 6531–6533 (2012)

    CrossRef  Google Scholar 

  23. Sedghi, A., Kapur, T., Luo, J., Mousavi, P., Wells, W.M.: Probabilistic image registration via deep multi-class classification: characterizing uncertainty. In: Greenspan, H., et al. (eds.) CLIP/UNSURE 2019. LNCS, vol. 11840, pp. 12–22. Springer, Cham (2019).

    CrossRef  Google Scholar 

  24. de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128–143 (2019)

    CrossRef  Google Scholar 

  25. Zhong, H., Chetty, I.J.: Caution must be exercised when performing deformable dose accumulation for tumors undergoing mass changes during fractionated radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 97(1), 182–183 (2016)

    CrossRef  Google Scholar 

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This work has received funding from the European Union’s Horizon 2020 Marie Skłodowska-Curie Actions under Grant Agreement No. 955956.

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Correspondence to A. Smolders .

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Smolders, A., Lomax, T., Weber, D.C., Albertini, F. (2022). Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham.

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