(Sunday, 7/29/2018) 1:00 PM - 1:55 PM
Room: Room 209
Purpose: Develop a convex, linearly constrained model, that generates leaf trajectories and a deliverable plan for sliding window based IMRT, that accounts for all MLC dynamic delivery constraints.
Methods: This novel optimization model is called Direct Leaf Trajectory Optimization (DLTO). The model is capable of converting all machine and MLC constraints for sliding window based IMRT into a linear convex format, such as minimum leaf gap, maximum leaf speed, and maximum leaf travel distance. The tongue and groove (T&G) effect was also incorporated directly into our model in a linear convex form, whereas other optimization models are unable to do so due to the nature of concavity. Delivery efficiency has also been included in our DLTO models, which can guarantee the most efficient delivery. We employed the model to generate 2D IMRT fluence, a facial portrait fluence map, and flat beam fluence delivered using a flattening-filter-free (FFF) beam model for testing purposes.
Results: For the study, dose distribution, machine deliverability, MU efficiency, and treatment time efficiency were assessed. Gamma passing rates for clinical IMRT plans, were all greater than 95%, while the facial portrait plan achieved a passing rate of 94%. For flat beam generation, results indicated that the dose uniformity of the DLTO model can match the dose uniformity of conventional treatment fields to within 2%, with minimal T&G effect, and with delivery efficiency that is comparable to that of a flattening-filter machine.
Conclusion: The DLTO model can produce accurate and MU efficient sliding window based IMRT plans. The treatment delivery time was acceptable for clinical workflow.
Keywords
Optimization, Inverse Planning
Taxonomy
TH- External beam- photons: IMRT dose optimization algorithms
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