### Abstract

A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting close volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.

Original language | English (US) |
---|---|

Pages (from-to) | 965-971 |

Number of pages | 7 |

Journal | Medical Physics |

Volume | 23 |

Issue number | 6 |

DOIs | |

State | Published - Jun 1996 |

Externally published | Yes |

### Fingerprint

### Keywords

- genetic algorithms
- optimization
- radiation therapy planning

### ASJC Scopus subject areas

- Biophysics

### Cite this

*Medical Physics*,

*23*(6), 965-971. https://doi.org/10.1118/1.597858

**A generic genetic algorithm for generating beam weights.** / Langer, Mark; Brown, Richard; Morrill, S.; Lane, R.; Lee, O.

Research output: Contribution to journal › Article

*Medical Physics*, vol. 23, no. 6, pp. 965-971. https://doi.org/10.1118/1.597858

}

TY - JOUR

T1 - A generic genetic algorithm for generating beam weights

AU - Langer, Mark

AU - Brown, Richard

AU - Morrill, S.

AU - Lane, R.

AU - Lee, O.

PY - 1996/6

Y1 - 1996/6

N2 - A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting close volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.

AB - A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting close volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.

KW - genetic algorithms

KW - optimization

KW - radiation therapy planning

UR - http://www.scopus.com/inward/record.url?scp=0029944195&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029944195&partnerID=8YFLogxK

U2 - 10.1118/1.597858

DO - 10.1118/1.597858

M3 - Article

C2 - 8798167

AN - SCOPUS:0029944195

VL - 23

SP - 965

EP - 971

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -