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Record W2036563255 · doi:10.1118/1.2965986

Sci‐Sat AM(1): Imaging‐02: Comparison between experimental measurements and Monte Carlo simulations for the off‐focal radiation in diagnostic x‐ray systems

2008· article· en· W2036563255 on OpenAlexaff
ESM Ali, DWO Rogers

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsCarleton University
Fundersnot available
KeywordsFocal lengthOpticsCardinal pointMonte Carlo methodPhysicsX-ray tubeRadiationAnodeElectronMaterials scienceLens (geology)Nuclear physicsElectrode

Abstract

fetched live from OpenAlex

In a typical x-ray tube, off-focal radiation is mainly generated by the backscattered electrons that re-enter the anode outside the focal spot. In an earlier study, the EGSnrc/BEAMnrc system was modified to be able to properly transport the anode backscattered electrons, and to tally their subsequent generation of off-focal x-rays. In the current study, a diagnostic system and a recent digital mammography system are simulated using the modified BEAMnrc code, and the simulation results are compared with experimental measurements from the literature. Simulation results show excellent agreement with experimental measurements for the spectral shape of both the primary and the off-focal components, and also for the integral off-focal-to-primary ratio. The spectrum of the off-focal component at the patient plane is softer than the primary, which causes a slight softening in the overall spectrum. For a given configuration, the off-focal component increases with tube voltage because of the increased probability for off-focal x-rays to escape the anode self-filtration and the total filtration. This study validates our earlier implementation of off-focal radiation in EGSnrc/BEAMnrc, and provides a well-benchmarked tool that simulates x-ray tubes more realistically. The macro to add this feature to BEAMnrc is available from the authors.

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 score0.240
Threshold uncertainty score0.477

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 head0.050
GPT teacher head0.318
Teacher spread0.268 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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