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Record W2035574074 · doi:10.1118/1.4740105

Sci-Thur PM: YIS - 08: The effect of copper conversion plates on low-Z target image quality

2012· article· en· W2035574074 on OpenAlexaff
David Parsons, JL Robar

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOpticsDetectorCopperMaterials sciencePlanarContrast-to-noise ratioImage sensorImage qualityCone beam computed tomographyPhotonNuclear medicinePhysicsComputed tomographyMedicineRadiologyImage (mathematics)

Abstract

fetched live from OpenAlex

Current generation electronic portal imaging devices (EPID) contain a 1.0 mm copper conversion plate to increase detection efficiency of a therapeutic megavoltage spectrum. When using these EPIDs for low-Z target imaging, the conversion plate largely attenuates the large populations of diagnostic energy photons, thereby decreasing the benefits of low-Z target imaging. In this work we measure directly the effect the variation in thickness of a copper conversion plate has on image quality in planar and cone beam computed tomography imaging. Monte Carlo modeling was used to quantify changes to the diagnostic spectrum and detector response for low-Z target beams generated with 2.35 and 7.00 MeV electrons incident on a carbon target. Planar contrast-to-noise ratio (CNR) measurements were made as a function of copper thickness. Cone beam computed tomography (CBCT) image CNR measurements were made as a function of dose both with and without the copper plate present in the EPID. The presence of copper in the EPID decreased the diagnostic photon population by up to 20% and suppressed the peak detector response at 60 kV by a factor of 6.4. Planar CNR was increased by a factor ranging from 1.4 to 4.0 with no copper present compared to 1.0 mm thickness. Increases in CBCT image CNR ranged from a factor of 1.3 to 2.1 with the copper plate removed. As a result of this we suggest that the copper conversion plate be removed from the EPID when used for low-Z target planar or CBCT imaging.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.367

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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

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