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SU‐FF‐T‐112: First Evaluation of a New Multicriteria Optimization Tool‐Investigation of Pareto‐Surfaces for IMRT Prostate Plans
Author(s) -
Uhrig M,
Thieke C,
Alonso F,
Küfer K,
Monz M,
Scherrer A,
Oelfke U
Publication year - 2005
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 - 10.1118/1.1997783
Subject(s) - pareto principle , multi objective optimization , homogeneity (statistics) , pareto optimal , standard deviation , pareto analysis , computer science , radiation treatment planning , mathematical optimization , interpolation (computer graphics) , mathematics , radiation therapy , statistics , artificial intelligence , medicine , radiology , motion (physics)
Purpose: The aim of this paper is a first evaluation of the performance of a new multi‐criteria optimization (MCO) tool developed for IMRT treatment planning Method and Materials: The new MCO‐tool computes a set of Pareto‐optimal plans by simultaneously minimizing dose indicators for tumor targets and considered organs at risk. For the tumor, the dose homogeneity in the target is maximized for a given, prescribed mean dose while relative deviations to upper equivalent uniform dose (EUD) limits are minimized for organs at risk. The exploration of the solution space is done with a visual navigation tool, which provides control bars for each defined dose indicator. The navigation tool uses real‐time interpolation to allow a smooth transition between the pre‐computed plans. As a clinical example we consider a simplified prostate case where the only structures to be optimized are the PTV, rectum and bladder. The complete database of plans is visualized using EUD‐values of the organs at risk and the standard deviation of the target dose as axes. This approximation of the 3D‐Pareto‐surface is then examined. The sensitivity of the navigation process on the individual dose indicators is analyzed in terms of the gradients on the Pareto‐surface. Moreover, effects of rescaling the target dose homogeneity in terms of TCP‐values are analyzed. Results: The database provided by the MCO optimization for the simplified prostate case can indeed be presented as a 3D‐Pareto‐surface. The sensitivity of the navigation process is well reflected by the respective gradient on the Pareto‐surface. The rescaling of the target dose homogeneity in terms of TCP values allows studying the sensitivity of the Pareto‐surface in terms of the assumed radio‐sensitivity of the tumor. Conclusion: A first evaluation of a new MCO‐tool has been successfully completed for a simplified prostate case with 3 mutually conflicting optimization criteria.