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Using fluence separation to account for energy spectra dependence in computing dosimetric a ‐ Si EPID images for IMRT fields
Author(s) -
Li Weidong,
Siebers Jeffrey V.,
Moore Joseph A.
Publication year - 2006
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 - 10.1118/1.2369468
Subject(s) - multileaf collimator , monte carlo method , fluence , convolution (computer science) , optics , dosimetry , collimator , beam (structure) , kernel (algebra) , physics , computational physics , materials science , mathematics , linear particle accelerator , nuclear medicine , computer science , statistics , artificial intelligence , artificial neural network , medicine , laser , combinatorics
This study develops a method to improve the dosimetric accuracy of computed images for an amorphous silicon flat‐panel imager. Radially dependent kernels derived from Monte Carlo simulations are convolved with the treatment‐planning system's energy fluence. Multileaf collimator (MLC) beam hardening is accounted for by having separate kernels for open and blocked portions of MLC fields. Field‐size‐dependent output factors are used to account for the field‐size dependence of scatter within the imager. Gamma analysis was used to evaluate open and sliding window test fields and intensity modulated patient fields. For each tested field, at least 99.6% of the points had γ < 1 with a 3%, 3‐mm criteria. With a 2%, 2‐mm criteria, between 81% and 100% of points had γ < 1 . Patient intensity modulated test fields had 94%–100% of the points with γ < 1 with a 2%, 2‐mm criteria for all six fields tested. This study demonstrates that including the dependencies of kernel and fluence on radius and beam hardening in the convolution improves its accuracy compared with the use of radial and beam‐hardening independent kernels; it also demonstrates that the resultant accuracy of the convolution method is sufficient for pretreatment, intensity modulated patient field verification.