Using fluence separation to account for energy spectra dependence in computing dosimetric a-Si EPID images for IMRT fields

Weidong Li, Jeffrey V. Siebers, Joseph A. Moore

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number008612MPH
Pages (from-to)4468-4480
Number of pages13
JournalMedical Physics
Volume33
Issue number12
DOIs
StatePublished - 2006
Externally publishedYes

Bibliographical note

Funding Information:
The authors wish to thank C. Beltran from the Mayo Clinic for providing patient IMRT fields which were used in this study. The authors also wish to thank Devon Murphy for carefully editing this manuscript. This work was sponsored in part by Varian Oncology Systems and the National Institutes of Health, Grant No. 1R01CA98524.

Keywords

  • Amorphous silicon
  • Convolution
  • Dose verification
  • EPID
  • Fluence
  • Kernel
  • Superposition

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