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Planning of beam intensity modulation using an advanced 3D dose calculation algorithm and a simulated annealing
1Oncology Physics Department, Western General Hospital, Edinburgh, UK.
Purpose:
The aim of this work was to develop a fast inverse planning algorithm that will calculate optimum beam intensity distributions and beam shapes, and to incorporate the algorithm into a three-dimensional CT planning system.
Method:
The algorithm is based on the technique of simulated annealing and produces beam intensity distributions that could in principle be implemented clinically, either by the use of compensators or dynamic multileaf collimation. Dose distributions are calculated using a voxel beam model based on a spherical co-ordinate system, and transformations are given allowing the dose to be determined at any point within the patient. The dose calculation algorithm calculates primary and scattered dose separately from a knowledge of tissue/air ratios and differential scatter/air ratios, and both are corrected for the presence of heterogeneities in three dimensions. Specific attention is given to the execution time of the algorithm, and the methods developed allow satisfactory results to be achieved in calculation times which are sufficiently fast to be used interactively in the planning system. Several objective functions have been developed and can be selected in a simple manner by the user. In general, these attempt to achieve a uniform dose within the target while limiting the dose to organs at risk, either by upper dose limits or by specifying constraints on their dose volume histograms.
Results:
The beam intensity distributions produced from the optimization have been used automatically by the forward planning system to produce three-dimensional dose distributions, and the results obtained in a number of clinical situations are presented.
Conclusions:
The inverse planning algorithm developed has been successfully incorporated into a three-dimensional planning system and is capable of producing beam intensity modulated distributions for clinical implementation. The execution time of the algorithm is sufficiently fast to be used as an optimization tool in an interactive forward planning system.