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Minimum-MU and Sparse-Energy-Layer (MMSEL) Constrained Inverse Optimization Method for Efficiently Deliverable PBS Plans

Y Lin1*, B Clasie2, T Liu1, M McDonald1, K Langen1, H Gao1, (1) Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, (2) Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA


(Wednesday, 7/17/2019) 1:45 PM - 3:45 PM

Room: 301

Purpose: Deliverable proton pencil beam scanning (PBS) plans are subject to the minimum constraint of monitor unit (MU), and the plan delivery time mainly depends on the number of proton energy layers in the plan. This work will develop the inverse optimization method for efficiently deliverable PBS plans.

Methods: The proposed minimum-MU and sparse-energy-layer (MMSEL) constrained inverse optimization method utilizes convex relaxations to deal with the nonconvexity of minimum-MU constraint and dose-volume constraints, and regularizes group sparsity in proton energy to minimize the number of energy layers. The tradeoff between plan quality and delivery efficiency (in terms of the number of used energy layers) is controlled by the objective weighting of group sparsity regularization. MMSEL consists of two steps: first solve for appropriate energy layers, and then with selected energy layers solve for the efficiently deliverable PBS plan. The solution algorithm for MMSEL is developed using alternating direction method of multipliers. Range and setup uncertainties are modelled by robust optimization.

Results: MMSEL was validated using representative prostate, lung and head-and-neck (HN) cases. The minimum-MU constraint was strictly enforced for all cases, so that all plans were deliverable. The number of energy layers was reduced by MMSEL to 78%, 76%, and 61% for prostate, lung and HN respectively, while the similar plan quality was achieved. The number of energy layers was reduced by MMSEL to 54%, 57%, and 37% for prostate, lung and HN respectively, while the plan quality was comprised and acceptable.

Conclusion: MMSEL is proposed to strictly enforce minimum-MU constraint and minimize the number of energy layers during inverse optimization for efficiently deliverable PBS plans. In particular, the preliminary results suggest MMSEL potentially enables roughly 25% to 40% reduction of energy layers while maintaining the similar plan quality.


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