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Explicit optimization of plan quality measures in intensity‐modulated radiation therapy treatment planning
Author(s) -
Engberg Lovisa,
Forsgren Anders,
Eriksson Kjell,
Hårdemark Björn
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12146
Subject(s) - radiation treatment planning , mathematical optimization , computer science , plan (archaeology) , histogram , dose volume histogram , convex optimization , mathematics , regular polygon , artificial intelligence , radiation therapy , medicine , geometry , archaeology , image (mathematics) , history
Purpose To formulate convex planning objectives of treatment plan multicriteria optimization with explicit relationships to the dose‐volume histogram (DVH) statistics used in plan quality evaluation. Methods Conventional planning objectives are designed to minimize the violation of DVH statistics thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses‐at‐volume) used to evaluate plan quality. In this study, we abandon the usual penalty‐function framework and propose planning objectives that more closely relate to DVH statistics. The proposed planning objectives are based on mean‐tail‐dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. Results We investigated the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives were generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses‐at‐volume was observed in plans optimized within the proposed framework. Conclusion The initial computational study indicates that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives than using the conventional approach.