Medial Axis Seeding of a Guided Evolutionary Simulated Annealing (GESA) Algorithm for Automated Gamma Knife Radiosurgery Treatment Planning

  • David Dean
  • Pengpeng Zhang
  • Andrew K. Metzger
  • Claudio Sibata
  • Robert J. Maciunas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)


We present a method to optimize Gamma KnifeTM (Elekta, Stockholm, Sweden) radiosurgery treatment planning. A Guided Evolutionary Simulated Annealing optimization algorithm is used to maximize the therapeutic benefit through a probability model that dissects a patient volume image into three components: normal, critical normal, and tumor tissue. This evolutionary optimization algorithm may be seeded randomly or via an automatically detected medial axis. We use indices of dose conformality, level, and homogeneity to evaluate the degree to which a treatment plan has been optimized. Two clinical examples compare the GESA algorithm with current manual methods. GESA optimization shows therapeutic advantage over the treatment team.s manual effort. We find that computation of treatment plans with more than 8 shots require initial medial axis seeding (i.e., shot: number, size, and position) to complete within 8 hours on our workstation.


Gamma Knife Medial Axis Shot Number Shot Size Meningioma Tumor 
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  1. 1.
    Shu, H., Yan, Y., Luo, L., Bao, X.: Three-dimensional optimization of treatment planning for gamma unit treatment system. Med. Phys., 25 (1998) 2352–2357.CrossRefGoogle Scholar
  2. 2.
    Gibon, D., Rousseau, J., Castelain, B., Blond, S., Vasseur, C., Marchandise, X.: Treatment Planning Optimization by Conjugate Gradients and Simulated Annealing Methods in Stereotactic Radiosurgery. Int. J. Radiation Oncology Biol. Phys. 33 (1995) 201–210.CrossRefGoogle Scholar
  3. 3.
    Wu, Q.J., and J. D Bourland “Morphology-guided radiosurgery treatment planning and optimization for multiple isocenters”, Med. Phys. 26, October, pp. 2151–2160, 1999.Google Scholar
  4. 4.
    Wagner, T.H., Taeil, M.E., Ma, S.F., Meeks, S.L., Bova, F.J., Brechner, B.L., Chen, Y., Buatti, J.M., Friedman, W.A., Foote, K.D., Bouchet, L.G., A Geometrically Based Method for Automated Radiosurgery Planning. Int. J. Radiation Oncology Biol. Phys., 48 (2000) 1599–1611.CrossRefGoogle Scholar
  5. 5.
    Leichtman, G.S., Aita, A.L., Goldman, H.W.: Automated Gamma Knife dose planning using polygon clipping and adaptive simulated annealing. Med. Phys., 27 (2000) 154–162.CrossRefGoogle Scholar
  6. 6.
    Yip, P.P.C., Pao, Y.: Combinatorial Optimization with Use of Guided Evolutionary Simulated Annealing., IEEE Trans. Neural Networks, 6 (1995) 290–295.CrossRefGoogle Scholar
  7. 7.
    Zhang, P., D. Dean, A. Metzger, and C. Sibata, “Optimization of Gamma Knife Treatment Planning via Guided Evolutionary Simulated Annealing”, Med. Phys., 28 (2001), in press.Google Scholar
  8. 8.
    Dean D, Metzger A, Duerk J, Kapur V, Zhang P, Chou H, Sibata D, and Wu J: Accuracy and Precision of Gamma Knife Procedure Planning and Outcomes Assessment. Computer Aided Surgery. 5 (1999) 63.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • David Dean
    • 1
  • Pengpeng Zhang
    • 2
  • Andrew K. Metzger
    • 1
  • Claudio Sibata
    • 3
  • Robert J. Maciunas
    • 1
  1. 1.Department of Neurological Surgery, and The Research Institute, University Hospitals of Cleveland, and Department of Neurological SurgeryCase Western Reserve UniversityClevelandUSA
  2. 2.Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUSA
  3. 3.Department of Radiation Oncology, and The Research Institute, University Hospitals of Cleveland, and Department of Radiation OncologyCase Western Reserve UniversityClevelandUSA

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