SU‐F‐T‐350: Continuous Leaf Optimization (CLO) for IMRT Leaf Sequencing

Purpose: To study a new step‐and‐shoot IMRT leaf sequencing model that avoids the two main pitfalls of conventional leaf sequencing: (1) target fluence being stratified into a fixed number of discrete levels and/or (2) aperture leaf positions being restricted to a discrete set of locations. These assumptions induce error into the sequence or reduce the feasible region of potential plans, respectively. Methods: We develop a one‐dimensional (single leaf pair) methodology that does not make assumptions (1) or (2) that can be easily extended to a multi‐row model. The proposed continuous leaf optimization (CLO) methodology takes in an existing set of apertures and associated intensities, or solution “seed,” and improves the plan without the restrictiveness of 1or (2). It then uses a first‐order descent algorithm to converge onto a locally optimal solution. A seed solution can come from models that assume (1) and (2), thus allowing the CLO model to improve upon existing leaf sequencing methodologies. Results: The CLO model was applied to 208 generated target fluence maps in one dimension. In all cases for all tested sequencing strategies, the CLO model made improvements on the starting seed objective function. The CLO model also was able to keep MUs low. Conclusion: The CLO model can improve upon existing leaf sequencing methods by avoiding the restrictions of (1) and (2). By allowing for more flexible leaf positioning, error can be reduced when matching some target fluence. This study lays the foundation for future models and solution methodologies that can incorporate continuous leaf positions explicitly into the IMRT treatment planning model. Supported by Cancer Prevention & Research Institute of Texas (CPRIT) ‐ ID RP150485