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Record W2045627293 · doi:10.1118/1.4860157

Improving the energy response of external beam therapy (EBT) GafChromic<sup>TM</sup> dosimetry films at low energies (≤100 keV)

2014· article· en· W2045627293 on OpenAlexafffund
Hamed Bekerat, Slobodan Dević, F DeBlois, Kawaljeet Singh, Arman Sarfehnia, Jan Seuntjens, Shelley Shih, Yu Xiang, David F. Lewis

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDosimetryMaterials scienceMonte Carlo methodBeam (structure)IrradiationRange (aeronautics)Energy (signal processing)ScannerPhotonActive layerLayer (electronics)PhysicsOpticsAtomic physicsNuclear physicsNuclear medicineNanotechnologyComposite materialThin-film transistor

Abstract

fetched live from OpenAlex

PURPOSE: Purpose of this work is to investigate the effects of varying the active layer composition of external beam therapy (EBT) GafChromic(TM) films on the energy dependence of the film, as well as try to develop a new prototype with more uniform energy response at low photon energies (⩽ 100 keV). METHODS: First, the overall energy response (S(AD, W)(Q)) of different commercial EBT type film models that represent the three different generations produced to date, i.e., EBT, EBT2, and EBT3, was investigated. Pieces of each film model were irradiated to a fixed dose of 2 Gy to water for a wide range of beam qualities and the corresponding S(AD, W)(Q) was measured using a flatbed document scanner. Furthermore, the DOSRZnrc Monte Carlo code was used to determine the absorbed dose to water energy dependence of the film, f(Q). Moreover, the intrinsic energy dependence, kbq(Q), for each film model was evaluated using the corresponding S(AD, W)(Q) and f(Q). In the second part of this study, the authors investigated the effects of changing the chemical composition of the active layer on SAD, W(Q). Finally, based on these results, the film manufacturer fabricated several film prototypes and the authors evaluated their S(AD, W)(Q). RESULTS: The commercial EBT film model shows an under response at all energies below 100 keV reaching 39% ± 4% at about 20 keV. The commercial EBT2 and EBT3 film models show an under response of about 27% ± 4% at 20 keV and an over response of about 16% ± 4% at 40 keV.S(AD, W)(Q) of the three commercial film models at low energies show strong correlation with the corresponding f(-) (1)(Q) curves. The commercial EBT3 model with 4% Cl in the active layer shows under response of 22% ± 4% at 20 keV and 6% ± 4% at about 40 keV. However, increasing the mass percent of chlorine makes the film more hygroscopic which may affect the stability of the film's readout. The EBT3 film prototype with 7.5% Si shows a significant improvement in the energy response at very low energies compared to the commercial EBT3 films with 4% Cl. It shows under response of 15% ± 5% at about 20 keV to 2% ± 5% at about 40 keV. However, according to the manufacturer, the addition of 7.5% Si as SiO2 adversely affected the viscosity of the active fluid and therefore affected the potential use in commercial machine coating. The latest commercial EBT3 film model with 7% Al as Al2O3 shows an overall improvement in SAD, W(Q) compared to previous commercial EBT3 films. It shows under response at all energies <100 keV, varying from 20% ± 4% at 20 keV to 6% ± 4% at 40 keV. CONCLUSIONS: The energy response of films in the energy range <100 keV can be improved by adjusting the active layer chemical composition. Removing bromine eliminated the over response at about 40 keV. The under response at energies ≤ 30 keV is improved by adding 7% Al to the active layer in the latest commercial EBT3 film models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.247
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations86
Published2014
Admission routes2
Has abstractyes

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