PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Fluence map optimisation for prostate cancer intensity modulated radiotherapy planning using iterative solution method

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Here we projected a model-based IMRT treatment plan to produce the optimal radiation dosage by considering that the maximum amount of prescribed dose should be delivered to the target without affecting the surrounding healthy tissues especially the OARs. Fluence mapping is used for inverse planning. This suggested method can generate global minima for IMRT plans with reliable plan quality among diverse treatment planners and to provide better safety for significant parallel OARs in an effective way. The whole methodology is having the capability to handles various objectives and to generate effective treatment procedures as validated with illustrations on the CORT dataset. For the validation of our methodology, we have compared our result with the two other approaches for calculating the objectives based on dosevolume bounds and found that in our methodology dose across the prostate and lymph nodes is maximum and the time required for the convergence is minimum.
Rocznik
Strony
201--209
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
  • Department of Electronics & Telecommunication Engineering, Bhilai Institute of Technology (Seth Balkrishan Memorial), Near Bhilai House, Durg, Chhattisgarh, 491001, India
  • Department of Electrical Engineering, Bhilai Institute of Technology (Seth Balkrishan Memorial), Near Bhilai House, Durg, Chhattisgarh, 491001, India
  • Department of Applied Physics, Bhilai Institute of Technology (Seth Balkrishan Memorial), Near Bhilai House, Durg, Chhattisgarh, 491001, India
Bibliografia
  • 1. Bortfeld T. IMRT: a review and preview. Phys Med Biol. 2006;51(13):R363-R379.
  • 2. Hatano K, Araki A, Sakai M, et al. Current status of intensity-modulated radiation therapy (IMRT). Int J Clin Oncol. 2007;12(6):408-415.
  • 3. Yu CX, Amies CJ, Svatos M. Planning and delivery of intensity-modulated radiation therapy. Med Phys. 2008:35(12):5233-5241.
  • 4. Shepard DM, Ferris MC, Olivera GH, et al. Optimizing and delivery of radiation therapy to cancer patients. Siam Review. 1999:41(4):721-744.
  • 5. Lim J, Ferris MC, Wright SJ, et al. An optimization framework for conformal radiation treatment planning. INFORMS J Comput. 2007;19(3):366-380.
  • 6. Morrill SM, Lane RG, Wong JA, et al. Dose-volume considerations with linear programming optimization. Med Phys. 1991;18(6):1201-1210.
  • 7. Breedveld S, Storchi PRM, Keijzer M, et al. Fast, multiple optimizations of quadratic dose objective functions in IMRT. Phys Med Biol. 2006;51(14):3569-3579.
  • 8. Censor Y, Ben-Israel A, Xiao Y, et al. On linear infeasibility arising in intensity-modulated radiation therapy inverse planning. Linear Algebr Appl. 2008;428(5-6):1406-1420.
  • 9. Rosen II, Lane RG, Morrill SM, et al. Treatment plan optimization using linear programming. Med Phys. 1991;18(2):141-152.
  • 10. Allen H. Designing radiotherapy plans with elastic constraints and interior point methods. Health Care Manag Sci. 2003;6(1):5-16.
  • 11. Bortfeld T, Bürkelbach J, Boesecke R, et al. Methods of image reconstruction from projections applied to conformation radiotherapy. Phys Med Biol. 1990;35(10):1423-1434.
  • 12. Xing L, Hamilton RJ, Spelbring D, et al. Fast iterative algorithms for three-dimensional inverse treatment planning. Med Phys. 1998:25(10):1845-1849.
  • 13. Xing L, Chen GTY. Iterative methods for inverse treatment planning. Phys Med Biol. 1996:41(10):2107-2123.
  • 14. Aleman DM, Mišić VV, Sharpe MB. Computational enhancements to fluence map optimization for total marrow irradiation using IMRT. Comput Oper Res. 2013;40(9):2167-2177.
  • 15. Aleman DM, Glaser D, Romeijn HE, et al. Interior point algorithms: guaranteed optimality for fluence map optimization IMRT. Phys Med Biol. 2010;55(18):5467-5482.
  • 16. Oskoorouchi MR, Ghaffari HR, Terlaky T, et al. An interior point constraint generation algorithm for semi-infinite optimization with health-care application. Oper Res. 2011;59(5):1184–1197.
  • 17. Hamacher KK, Küfer K-H. Inverse radiation therapy planning—a multiple objective optimization approach. Discrete Appl Math. 2002;118(1):145-161.
  • 18. Halabi T, Craft D, Bortfeld T. Dose-volume objectives in multi-criteria optimization. Phys Med Biol. 2006;51(15):3809-3818.
  • 19. Deasy JO. Multiple local minima in radiotherapy optimization problems with dose-volume constraints. Med Phys. 1997:24(7):1157-1161.
  • 20. Wu C, Jeraj R, Mackie TR. The method of intercepts in parameter space for the analysis of local minima caused by dose-volume constraints. Phys Med Biol. 2003;48(11):149-157.
  • 21. Wu Q, Mohan R. Multiple local minima in IMRT optimization based on dose-volume criteria. Med Phys. 2002;29(7):1514-1527.
  • 22. Romeijn HE, Ahuja RA, Dempsey JF, et al. A new linear programming approach to radiation therapy treatment planning problems. Oper Res. 2006;54(2):201-216.
  • 23. Zhang Y, Merritt M. Dose–volume-based IMRT fluence optimization: a fast least-squares approach with differentiability. Linear Algebr Appl. 2008;428(5-6):1365-1387.
  • 24. Spirou SV, Chui CS. A gradient inverse planning algorithm with dose-volume constraints. Med Phys. 1998:25(3): 321-333.
  • 25. Cotrutz C, Xing L. Using voxel-dependent importance factors for interactive DVH-based dose optimization. Phys Med Biol. 2002:47(10):1659-1669.
  • 26. Yang Y, Xing L. Inverse treatment planning with adaptively evolving voxel-dependent penalty scheme. Med Phys. 2004;31(10):2839-2844.
  • 27. Shou Z, Yang Y, Cotrutz C, et al. Quantification of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space. Phys Med Biol. 2005;(7):1469-1482.
  • 28. Zhaosong Lu, Zhang Y. Penalty decomposition methods for l0-norm minimization. arXiv preprint, arXiv:1008.53722, 2010.
  • 29. Craft D, Bangert M, Long T, et al. Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset. GigaScience. 2014;3(1):2047–217X–3–37.
  • 30. Schmidt M, Berg E, Friedlander M, et al. Optimizing costly functions with simple constraints: A limited-memory projected quasinewton algorithm. In: Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics Artificial Intelligence and Statistics. 2009. pp. 456-463
  • 31. Schmidt M. minConf: Projection methods for optimization with simple constraints in Matlab. 2008. [Online]. Available: http://www.cs.ubc.ca/~schmidtm/Software/minConf.html.
  • 32. Grant M. Boyd S. CVX: Matlab software for disciplined convex programming,version 2.1. 2014. [Online]. Available: http://cvxr.com/cvx.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-992374da-4b93-4dae-b04d-2ffc574a2617
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.