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Record W2053822726 · doi:10.1118/1.3110107

Solid Water as phantom material for dosimetry of electron backscatter using low‐energy electron beams: A Monte Carlo evaluation

2009· article· en· W2053822726 on OpenAlexafffund
James C. L. Chow, Amir Owrangi

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan UniversityPrincess Margaret Cancer CentreUniversity of TorontoUniversity of Waterloo
FundersNational Research Council Canada
KeywordsMonte Carlo methodImaging phantomDosimetryElectronCathode rayMaterials scienceBackscatter (email)Range (aeronautics)FluenceStopping powerEnergy-dispersive X-ray spectroscopyAbsorbed doseRadiationAtomic physicsPhysicsIrradiationOpticsNuclear medicineScanning electron microscopeNuclear physics

Abstract

fetched live from OpenAlex

This study evaluated the dosimetry of electron backscatter when Solid Water is used to substitute water as phantom in electron radiotherapy. Monte Carlo simulation (EGSnrc-based code) was employed to predict electron energy spectra and depth doses for the 0.5 and 1 cm of Solid Water and water slabs above 3 mm of lead (Pb) layers using electron beams with energies of 4 and 6 MeV. For comparison, Monte Carlo simulations were repeated with Pb layers taken out from the phantoms using the same experimental configuration. Analyses on electron energy spectra for the 4 and 6 MeV electron beams showed that deviations of electron energy distributions between the Solid Water and water phantom were more significant in the high-energy range (i.e., close to the maximal electron energy) than the lower range corresponding to the electron backscatter. These deviations of electron energy spectra varied with depth and were mainly due to the electron fluence or beam attenuation. Dosimetry results from Monte Carlo simulations showed that the Solid Water phantom had lower depth dose compared to water with the same experimental setup. For the 4 MeV electron beams with 0.5 cm of Solid Water, depth doses were 1.8%-3.9% and 2.3%-4.4% lower than those in water, with and without the Pb layer underneath, respectively. Thicker Solid Water of 1 cm resulted in different decreases in depth doses of 1.8%-4.6% (with Pb) and 2.3%-4.4% (without Pb) compared to water. For higher nominal electron beam energy of 6 MeV with 0.5 cm of Solid Water, depth doses decreased 1.7%-2.9% (with Pb) and 1.6%-2.1% (without Pb) compared to water. These decreases in depth doses changed to 1.7%-3.7% (with Pb) and 1.7%-3% (without Pb) when the thickness of Solid Water was increased to 1 cm. The dosimetry data in this study are useful in determining the correction factor when using Solid Water to substitute water for the electron backscatter measurement in electron radiotherapy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.332
Teacher spread0.323 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations7
Published2009
Admission routes2
Has abstractyes

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