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Scatter corrections for cone beam optical CT

2009· article· en· W2088554720 on OpenAlexafffund
Tim Olding, Oliver Holmes, L J Schreiner

Bibliographic record

VenueJournal of Physics Conference Series · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsCone beam ctCone (formal languages)OpticsBeam (structure)PhysicsCone beam computed tomographyComputer scienceMedicineComputed tomographyRadiologyAlgorithm

Abstract

fetched live from OpenAlex

Cone beam optical computed tomography (OptCT) employing the VISTA scanner (Modus Medical, London, ON) has been shown to have significant promise for fast, three dimensional imaging of polymer gel dosimeters. One distinct challenge with this approach arises from the combination of the cone beam geometry, a diffuse light source, and the scattering polymer gel media, which all contribute scatter signal that perturbs the accuracy of the scanner. Beam stop array (BSA), beam pass array (BPA) and anti-scatter polarizer correction methodologies have been employed to remove scatter signal from OptCT data. These approaches are investigated through the use of well-characterized phantom scattering solutions and irradiated polymer gel dosimeters. BSA corrected scatter solutions show good agreement in attenuation coefficient with the optically absorbing dye solutions, with considerable reduction of scatter-induced cupping artifact at high scattering concentrations. The application of BSA scatter corrections to a polymer gel dosimeter lead to an overall improvement in the number of pixel satisfying the (3%, 3mm) gamma value criteria from 7.8% to 0.15%.

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: none
Teacher disagreement score0.904
Threshold uncertainty score0.391

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 communication0.0000.001
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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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