Abstract
Purpose: The development and testing of a tool for the inclusion of delivery time as a parameter in plan optimization. Methods: We developed an algorithm that estimates the time required for the machine and personnel movements required to deliver a treatment plan on a linear accelerator. We included dose rate, leaf motion, collimator motion, gantry motion, and couch motions (including time to enter the room to rotate the couch safely). Vault-specific parameters to account for time to enter and perform couch angle adjustments were also included. This algorithm works for static, step and shoot IMRT, and VMAT beams photon beams and for fixed electron beams. This was implemented as a script in our treatment planning system. We validated the estimator against actual recorded delivery time from our R and V system as well as recorded times from our IMRT QA delivery. Results: Data was collected (Figure 1) for 12 treatment plans by examining the R and V beam start times, and by manually timing the QA treatment for a reference, but the QA measurements were only significant to the nearest minute. The average difference between the estimated and R and V times was 15%, and 11% when excluding the majormore » outliers. Outliers arose due to respiratory aides and gating techniques which could not be accounted for in the estimator. Conclusion: Non-mechanical factors such as the time a therapist needs to walk in and out of the room to adjust the couch needed to be fine-tuned and cycled back into the algorithm to improve the estimate. The algorithm has been demonstrated to provide reasonable and useful estimates for delivery time. This estimate has provided a useful additional input for clinical decision-making when comparing several potential radiation treatment options.« less
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