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Record W2037543550 · doi:10.1118/1.4894988

Poster — Thur Eve — 02: Measurement of CT radiation profile width using Fuji CR imaging plate raw data

2014· article· en· W2037543550 on OpenAlexaff
Thorarin A. Bjarnason, Chengwei Yang

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyInterior Health
Fundersnot available
KeywordsClipping (morphology)Collimated lightDICOMFlat panel detectorOpticsComputed radiographyMaterials scienceImage qualityDetectorCollimatorProjectorDigital radiographyDose profileRange (aeronautics)Nuclear medicineRadiographyComputer sciencePhysicsMedicineLaserArtificial intelligenceBeam (structure)RadiologyImage (mathematics)

Abstract

fetched live from OpenAlex

Measuring the CT collimation width and assessing the shape of the overall profile is a relatively straightforward quality control (QC) measure that impacts both image quality and patient dose, and is often required at acceptance and routine testing. Most CT facilities have access to computed radiography (CR) systems, so performing CT collimation profile assessments using CR plates requires no additional equipment. Previous studies have shown how to effectively use CR plates to measure the radiation profile width. However, a major limitation of the previous work is that the full dynamic range of CR detector plates are not used, since the CR processing technology reduces the dynamic range of the DICOM output to 2 10 , requiring the sensitivity and latitude settings of CR reader to be adjusted to prevent clipping of the CT profile data. Such adjustments to CR readers unnecessarily complicate the QC procedure. These clipping artefacts hinder the ability to accurately assess CT collimation width because the full‐width at half maximum value of the penumbras are not properly determined if the maximum dose of the profile is not available. Furthermore, any inconsistencies in the radiation profile shape are lost if the profile plateau is clipped off. In this work we developed an opensource Matlab script for straightforward CT profile width measurements using raw CR data that also allows assessment of the profile shape without clipping, and applied this approach during CT QC.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.591

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 head0.028
GPT teacher head0.261
Teacher spread0.232 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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