SU‐E‐T‐33: A Feasibility‐Seeking Algorithm Applied to Planning of Intensity Modulated Proton Therapy: A Proof of Principle Study

Abstract

Purpose:To investigate the applicability of feasibility‐seeking cyclic orthogonal projections to the field of intensity modulated proton therapy (IMPT) inverse planning. Feasibility of constraints only, as opposed to optimization of a merit function, is less demanding algorithmically and holds a promise of parallel computations capability with non‐cyclic orthogonal projections algorithms such as string‐averaging or block‐iterative strategies.Methods:A virtual 2D geometry was designed containing a C‐shaped planning target volume (PTV) surrounding an organ at risk (OAR). The geometry was pixelized into 1 mm pixels. Four beams containing a subset of proton pencil beams were simulated in Geant4 to provide the system matrix A whose elements a_ij correspond to the dose delivered to pixel i by a unit intensity pencil beam j. A cyclic orthogonal projections algorithm was applied with the goal of finding a pencil beam intensity distribution that would meet the following dose requirements: D_OAR < 54 Gy and 57 Gy < D_PTV < 64.2 Gy. The cyclic algorithm was based on the concept of orthogonal projections onto half‐spaces according to the Agmon‐Motzkin‐Schoenberg algorithm, also known as ‘ART for inequalities’.Results:The cyclic orthogonal projections algorithm resulted in less than 5% of the PTV pixels and less than 1% of OAR pixels violating their dose constraints, respectively. Because of the abutting OAR‐PTV geometry and the realistic modelling of the pencil beam penumbra, complete satisfaction of the dose objectives was not achieved, although this would be a clinically acceptable plan for a meningioma abutting the brainstem, for example.Conclusion:The cyclic orthogonal projections algorithm was demonstrated to be an effective tool for inverse IMPT planning in the 2D test geometry described. We plan to further develop this linear algorithm to be capable of incorporating dose‐volume constraints into the feasibility‐seeking algorithm.