On the implementation of dose-volume objectives in gradient algorithms for inverse treatment planning.

A method that allows a straightforward implementation of dose-volume constraints in gradient algorithms for inverse treatment planning is presented. The method is consistent with the penalty function approach, which requires the formulation of an objective function with penalty terms proportional to the magnitudes of constraint violations. Dose constraints with respect to minimum and maximum target dose levels are incorporated in quadratic, dose-penalty terms. Analogously, quadratic volume-penalty terms in the objective function reflect the violation of dose-volume constraints imposing limits on the fractions of healthy organ volumes that can be irradiated above specified dose levels. It has been demonstrated that within the framework of this formulation neither modified objective functions nor finite difference gradient calculations are necessary for the incorporation of gradient minimization algorithms. As an example, a simple steepest descent algorithm is presented along with its application to illustrate prostate and lung cases.

[1]  I I Rosen,et al.  Constrained simulated annealing for optimized radiation therapy treatment planning. , 1990, Computer methods and programs in biomedicine.

[2]  P Kijewski,et al.  The reliability of optimization under dose-volume limits. , 1993, International journal of radiation oncology, biology, physics.

[3]  J. Battista,et al.  Normal tissue complication probabilities: dependence on choice of biological model and dose-volume histogram reduction scheme. , 2000, International journal of radiation oncology, biology, physics.

[4]  R Mohan,et al.  Algorithms and functionality of an intensity modulated radiotherapy optimization system. , 2000, Medical physics.

[5]  J. Lyman.  Complication Probability as Assessed from Dose-Volume Histograms , 1985 .

[6]  M. Langer,et al.  Optimization of beam weights under dose-volume restrictions. , 1987, International journal of radiation oncology, biology, physics.

[7]  A. Brahme,et al.  Optimized radiation therapy based on radiobiological objectives. , 1999, Seminars in radiation oncology.

[8]  G Starkschall,et al.  Treatment planning using a dose-volume feasibility search algorithm. , 2001, International journal of radiation oncology, biology, physics.

[9]  J. Deasy.  Multiple local minima in radiotherapy optimization problems with dose-volume constraints. , 1997, Medical physics.

[10]  A Brahme,et al.  Individualizing cancer treatment: biological optimization models in treatment planning and delivery. , 2001, International journal of radiation oncology, biology, physics.

[11]  T D Solberg,et al.  Comparative behaviour of the dynamically penalized likelihood algorithm in inverse radiation therapy planning. , 2001, Physics in medicine and biology.

[12]  I. Rosen,et al.  Very fast simulated reannealing in radiation therapy treatment plan optimization. , 1995, International journal of radiation oncology, biology, physics.

[13]  B G Fallone,et al.  An active set algorithm for treatment planning optimization. , 1997, Medical physics.

[14]  A Niemierko,et al.  Random search algorithm (RONSC) for optimization of radiation therapy with both physical and biological end points and constraints. , 1992, International journal of radiation oncology, biology, physics.

[15]  Elijah Polak,et al.  Computational methods in optimization , 1971 .

[16]  C C Ling,et al.  Clinical experience with intensity modulated radiation therapy (IMRT) in prostate cancer. , 2000, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  M. Goitein,et al.  Tolerance of normal tissue to therapeutic irradiation. , 1991, International journal of radiation oncology, biology, physics.

[18]  A Brahme,et al.  Development of Radiation Therapy Optimization , 2000, Acta oncologica.

[19]  J. Llacer.  Inverse radiation treatment planning using the Dynamically Penalized Likelihood method. , 1997, Medical physics.

[20]  M. Goitein,et al.  Fitting of normal tissue tolerance data to an analytic function. , 1991, International journal of radiation oncology, biology, physics.

[21]  S. Spirou,et al.  A gradient inverse planning algorithm with dose-volume constraints. , 1998, Medical physics.

[22]  I I Rosen,et al.  Treatment planning optimization using constrained simulated annealing. , 1991, Physics in medicine and biology.

[23]  K S Lam,et al.  Comparison of simulated annealing algorithms for conformal therapy treatment planning. , 1995, International journal of radiation oncology, biology, physics.

[24]  T Bortfeld,et al.  Optimized planning using physical objectives and constraints. , 1999, Seminars in radiation oncology.

[25]  J. Shapiro,et al.  Large scale optimization of beam weights under dose-volume restrictions. , 1990, International journal of radiation oncology, biology, physics.