TY - JOUR
T1 - Fast and accurate sensitivity analysis of IMPT treatment plans using Polynomial Chaos Expansion
AU - Perkó, Zoltá
AU - Van Der Voort, Sebastian R.
AU - Van De Water, Steven
AU - Hartman, Charlotte M.H.
AU - Hoogeman, Mischa
AU - Lathouwers, Danny
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Polynomial Chaos
KW - proton therapy
KW - Range error
KW - Robust optimization
KW - Sensitivity
KW - Setup error
KW - Uncertainty
UR - http://resolver.tudelft.nl/uuid:b8fc4d46-e0f6-49ec-8eb2-d01176e8393a
UR - http://www.scopus.com/inward/record.url?scp=84975041431&partnerID=8YFLogxK
U2 - 10.1088/0031-9155/61/12/4646
DO - 10.1088/0031-9155/61/12/4646
M3 - Article
AN - SCOPUS:84975041431
VL - 61
SP - 4646
EP - 4664
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
SN - 0031-9155
IS - 12
ER -