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Record W2070730174 · doi:10.1118/1.2905028

Relationship between and stopping‐power ratios for flattening filter free accelerators: A Monte Carlo study

2008· article· en· W2070730174 on OpenAlexafffund
Guoming Xiong, D. W. O. Rogers

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlatteningMonte Carlo methodBeam (structure)Filter (signal processing)Computational physicsOpticsPhysicsStopping powerMaterials scienceMathematicsStatisticsComputer scienceDetector

Abstract

fetched live from OpenAlex

The relationship between the photon beam quality specifier %dd(10)x and the Spencer-Attix water water to air restricted mass collision stopping-power ratio, (L/rho))air(water), is studied using Monte Carlo simulation with realistic beams in contrast to the previously used realistic but uniform spectra from an isotropic point source. The differences between accelerators with and without flattening filters are investigated since flattening filter free accelerators appear to be useful for IMRT. Our results show that the standard relationship between %dd(10)x and (L/rho)air(water), which is used in the TG-51 protocol to calculate the quality conversion factor kQ, is acceptable for beams with or without a flattening filter with a maximum error of 0.4%, although a fit to the new data would reduce the maximum error to 0.2%. Reasons for differences between the individual values of %dd(10)x and (L/ rho)air(water) with and without a flattening filter are studied. Specifically the differences due to the softening of the beam, the change in shape of the profile, and the inclusion of radial variations in the photon energy spectra, are investigated. It is shown that if TPR10(20) is used as a beam quality specifier, there are two different relationships between TPR10(20) and (L/rho)air(water) which differ by 0.4%-1%. When using TPR10(20) as a beam quality specifier in a beam without a flattening filter, one should subtract 0.5% from the value of kQ for a given value of TPR10(20).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.046
GPT teacher head0.316
Teacher spread0.270 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations54
Published2008
Admission routes2
Has abstractyes

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