Abstract
Intensity-Modulated Radiation Therapy (IMRT) is a method for treating cancers by aiming radiation to cancer tumor while minimizing radiation to organs-at-risk. Usually, radiation is aimed from a particle accelerator, mounted on a robot manipulator. Computationally finding the correct treatment plan for a target volume is often an exhaustive combinatorial search problem, and traditional optimization methods have not yielded real-time feasible results. Aiming to automate the beam orientation and intensity-modulation process, we introduce a novel set of techniques leveraging (i) pattern recognition, (ii) monte carlo evaluations, (iii) game theory, and (iv) neuro-dynamic programming. We optimize a deep neural network policy that guides Monte Carlo simulations of promising beamlets. Seeking a saddle equilibrium, we let two fictitious neural network players, within a zero-sum Markov game framework, alternatingly play a best response to their opponent’s mixed strategy profile. During inference, the optimized policy predicts feasible beam angles on test target volumes. This work merges the beam orientation and fluence map optimization subproblems in IMRT sequential treatment planning system into one pipeline. We formally introduce our approach, and present numerical results for coplanar beam angles on prostate cases.
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Aleman, D.M., Kumar, A., Ahuja, R.K., Romeijn, H.E., Dempsey, J.F.: Neighborhood search approaches to beam orientation optimization in intensity modulated radiation therapy treatment planning. J. Glob. Optim. 42(4), 587–607 (2008)
Ogunmolu, O., Gans, N., Summers, T.: Minimax iterative dynamic game: application to nonlinear robot control tasks. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6919–6925 (2018). https://doi.org/10.1109/IROS.2018.8594037
Heinrich, J., Lanctot, M., Silver, D.: Fictitious self-play in extensive-form games. In: International Conference on Machine Learning, pp. 805–813 (2015)
Craft, D.: Local beam angle optimization with linear programming and gradient search. Phys. Med. Biol. 52(7), N127 (2007)
Bertsimas, D., Cacchiani, V., Craft, D., Nohadani, O.: A hybrid approach to beam angle optimization in intensity-modulated radiation therapy. Comput. Oper. Res. 40(9), 2187–2197 (2013)
Jia, X., Men, C., Lou, Y., Jiang, S.B.: Beam orientation optimization for intensity modulated radiation therapy using adaptive L2,1-minimization. Phys. Med. Biol. 56(19), 6205–6222 (2011)
Bortfeld, T., Schlegel, W.: Optimization of beam orientations in radiation therapy: some theoretical considerations. Phys. Med. Biol. 38(2), 291 (1993)
Djajaputra, D., Wu, Q., Wu, Y., Mohan, R.: Algorithm and performance Of A clinical imrt beam-angle optimization system. Phys. Med. Biol. 48(19), 3191 (2003)
Pugachev, A., Xing, L.: Computer-assisted selection of coplanar beam orientations in intensity-modulated radiation therapy. Phys. Med. Biol. 46(9), 2467 (2001)
Li, Y., Yao, J., Yao, D.: Automatic beam angle selection in IMRT planning using genetic algorithm. Phys. Med. Biol. 49(10), 1915 (2004)
Wang, C., Dai, J., Hu, Y.: Optimization of beam orientations and beam weights for conformal radiotherapy using mixed integer programming. Phys. Med. Biol. 48(24), 4065 (2003)
Lim, G.J., Ferris, M.C., Wright, S.J., Shepard, D.M., Earl, M.A.: An optimization framework for conformal radiation treatment planning. INFORMS J. Comput. 19(3), 366–380 (2007)
D’Souza, W.D., Meyer, R.R., Shi, L.: Selection of beam orientations in intensity-modulated radiation therapy using single-beam indices and integer programming. Phys. Med. Biol. 49(15), 3465 (2004)
Hou, Q., Wang, J., Chen, Y., Galvin, J.M.: Beam orientation optimization for IMRT by a hybrid method of the genetic algorithm and the simulated dynamics. Med. Phys. 30(9), 2360–2367 (2003)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Agrawal, R.: Sample mean based index policies by O (log n) regret for the multi-armed bandit problem. Adv. Appl. Probab. 27(4), 1054–1078 (1995)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947 (2000)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)
Chung, M., Buro, M., Schaeffer, J.: Monte Carlo planning in RTS games. In: CIG. Citeseer (2005)
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354 (2017)
Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)
Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: International Conference on Computers and Games (2006)
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: European Conference on Machine Learning (2006)
Basar, T., Olsder, G.J.: Dynamic Noncooperative Game Theory, vol. 23. SIAM, Philadelphia (1999)
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Ogunmolu, O., Folkerts, M., Nguyen, D., Gans, N., Jiang, S. (2020). Deep BOO! Automating Beam Orientation Optimization in Intensity-Modulated Radiation Therapy. In: Morales, M., Tapia, L., Sánchez-Ante, G., Hutchinson, S. (eds) Algorithmic Foundations of Robotics XIII. WAFR 2018. Springer Proceedings in Advanced Robotics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-44051-0_20
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