![]() ![]() All constraints in this optimization system are soft constraints, which means that the optimization process can accept a treatment plan even though a constraint is not fulfilled. which dose constraint that should be prioritized. This weight factor indicates how important the dose constraint is in relation to the other dose constraints, i.e. For each dose constraint a weight factor from 0 to 100% has to be chosen. ![]() For organs at risk the maximum absorbed dose accepted is set and it is also possible to set constraints as points in a dose–volume histogram (DVH). Two dose constraints are set for target volumes the minimum and the maximum absorbed dose desired. The desired dose distribution is given to the optimization algorithm in terms of dose constraints for the delineated volumes. In order to use the benefits of IMRT, all organs at risk and targets have to be carefully delineated in three dimensions. General investigations in finding the optimal number of beams have been published earlier [ #Purpose of multileaf collimator in radiotherapy how to#It will also give useful information to users on how to adjust different parameters to achieve a desired dose distribution. The study will illustrate how the optimization algorithm deals with the variation of these parameters. Dose escalation to the target volume, the target volume in the build-up region and the way of prescribing the target dose were also investigated. MLC directions of movement), and constraints and weight factors have been investigated. Parameters such as number of beams, collimator angles (i.e. The aim of this study was to gain knowledge about the influence of different parameters on the dose distribution and how to use the optimization algorithm in an optimum way. The optimized fluence distributions are transformed into actual fluence distributions, possible for dynamic multileaf collimator (MLC) delivery, before the final dose distribution is calculated. A number of fixed fields and their gantry and collimator angles have to be set prior to the optimization of the fluence distributions. This study investigates a dose-based conjugated gradient optimization method (implemented in the CadPlan/Helios system) applied for head and neck tumour treatment planning. ![]()
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