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Record W2079586759 · doi:10.1118/1.3611665

SU‐E‐I‐91: Use of the ACR CT Accreditation Phantom for Routine Quality Assurance of CBCT Imaging Systems in a Radiotherapy Environment

2011· article· en· W2079586759 on OpenAlexaff
Maritza A. Hobson, William Parker, E Soisson

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsImaging phantomQuality assuranceDICOMImage qualityNuclear medicineCone beam computed tomographyImage resolutionMedical imagingSoftwareComputer scienceMedical physicsMedicineArtificial intelligenceComputed tomographyComputer visionRadiologyImage (mathematics)

Abstract

fetched live from OpenAlex

Purpose: Image guided radiation therapy using cone‐beam computed tomography (CBCT) is becoming routine practice in modern radiation therapy. The purpose of this work was to develop an imaging QA program for CT and CBCT units in our department based on the ACR CT accreditation phantom (model 464, Gammex‐RMI). It has four testing modules permitting one to test CT number accuracy, slice width, low contrast resolution, image uniformity, in plane distance accuracy, and high contrast resolution reproducibly with suggested window/levels for image analysis. Methods: Baseline values were obtained from images acquired on a Phillips Brilliance Big Bore CT simulator and CBCT images acquired on three Varian OBIs. DICOM images were exported and analyzed with software (Automated CT Software, Gammex‐RMI) or manually (OsiriX). Baseline values will be used to ensure that image quality stays consistent quarterly. Results: Initial CT simulator images showed that image quality was within ACR guidelines for all tested scanning protocols. Due to image noise and reconstruction artifacts, manual analysis should be used for future analysis of CBCT images. Image analysis from two OBIs showed that the HU calibration had drifted or was not properly calibrated, while the third OBI showed reasonable agreement with accepted values. All three OBIs were unable to distinguish the low contrast resolution plugs, had the same high contrast resolution, were within 0.7 mm of the accepted in plane resolution, and were within 0.5 mm of the nominal slice width. Conclusions: Preliminary analysis shows that the ACR phantom could be modified to be more useful in evaluating the low contrast resolution of CBCT systems. Suggestions will be made as to how ACR guidelines for image analysis could be modified to better suit CBCT systems, such as for image uniformity. It is expected to eventually incorporate all departmental CT imaging systems in into this imaging QA program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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