Fast and accurate sensitivity analysis of IMPT treatment plans using Polynomial Chaos Expansion

Journal Article (2016)
Author(s)

Z. Perkó (TU Delft - RST/Reactor Physics and Nuclear Materials)

Sebastian R. van der Voort (TU Delft - Applied Sciences, Erasmus MC)

Steven Van De Water (Erasmus MC)

Charlotte M.H. Hartman (Erasmus MC, TU Delft - ImPhys/Practicum support)

M.S. Hoogeman (Erasmus MC)

D. Lathouwers (TU Delft - RST/Reactor Physics and Nuclear Materials)

Research Group
RST/Reactor Physics and Nuclear Materials
Copyright
© 2016 Z. Perko, S.R. van der Voort, Steven Van De Water, C.M.H. Hartman, M.S. Hoogeman, D. Lathouwers
DOI related publication
https://doi.org/10.1088/0031-9155/61/12/4646
More Info
expand_more
Publication Year
2016
Language
English
Copyright
© 2016 Z. Perko, S.R. van der Voort, Steven Van De Water, C.M.H. Hartman, M.S. Hoogeman, D. Lathouwers
Research Group
RST/Reactor Physics and Nuclear Materials
Issue number
12
Volume number
61
Pages (from-to)
4646-4664
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The highly conformal planned dose distribution achievable in intensity modulated proton therapy (IMPT) can severely be compromised by uncertainties in patient setup and proton range. While several robust optimization approaches have been presented to address this issue, appropriate methods to accurately estimate the robustness of treatment plans are still lacking. To fill this gap we present Polynomial Chaos Expansion (PCE) techniques which are easily applicable and create a meta-model of the dose engine by approximating the dose in every voxel with multidimensional polynomials. This Polynomial Chaos (PC) model can be built in an automated fashion relatively cheaply and subsequently it can be used to perform comprehensive robustness analysis. We adapted PC to provide among others the expected dose, the dose variance, accurate probability distribution of dose-volume histogram (DVH) metrics (e.g. minimum tumor or maximum organ dose), exact bandwidths of DVHs, and to separate the effects of random and systematic errors. We present the outcome of our verification experiments based on 6 head-and-neck (HN) patients, and exemplify the usefulness of PCE by comparing a robust and a non-robust treatment plan for a selected HN case. The results suggest that PCE is highly valuable for both research and clinical applications.

Files

Fast_and_Accurate_Sensitivity_... (pdf)
(pdf | 1.42 Mb)
- Embargo expired in 26-05-2017
License info not available