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Record W1001090502 · doi:10.1118/1.4924715

SU‐E‐T‐354: Efficient and Enhanced QA Testing of Linear Accelerators Using a Real‐Time Beam Monitor

2015· article· en· W1001090502 on OpenAlexaff
James J. Jung, Makan Farrokhkish, B Norrlinger, Y. Wang, Robert K. Heaton, David A. Jaffray, Mohammad Saiful Islam

Bibliographic record

VenueMedical Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsLinear particle acceleratorIonization chamberCalibrationDosimetryBeam (structure)CollimatorOpticsMonitor unitPhysicsAperture (computer memory)Sensitivity (control systems)Quality assuranceLinearityNuclear medicineAcousticsElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Purpose: To investigate the feasibility of performing routine QA tests of linear accelerators (Linac) using the Integral Quality Monitoring (IQM) system. The system, consisting of a 1‐D sensitivity gradient large area ion‐chamber mounted at the collimator, allows automatic collection and analysis of beam data. Methods: The IQM was investigated to perform several QA constancy tests, similar to those recommended by AAPM TG142, of a Linac including: beam output, MLC calibration, beam symmetry, relative dose factor (RDF), dose linearity, output as a function of gantry angle and dose rate. All measurements by the IQM system accompanied a reference measurement using a conventional dosimetry system and were performed on an Elekta Infinity Linac with Agility MLC. The MLC calibration check is done using a Picket‐Fence type 2×10cm2 field positioned at different off‐axis locations along the chamber gradient. Beam symmetry constancy values are established by signals from an 4×4cm2 aperture located at various off‐axis positions; the sensitivity of the test was determined by the changes in the signals in response to a tilt in the beam. The data for various square field sizes were used to develop a functional relationship with RDF. Results: The IQM tracked the beam output well within 1% of the reference ion‐chamber readings. The Picket‐Fence type field test detected a 1mm shift error of one MLC bank. The system was able to detect 2.5% or greater beam asymmetry. The IQM results for all other QA tests were found to agree with the reference values to within 0.5%. Conclusion: It was demonstrated that the IQM system can effectively monitor the Linac performance parameters for the purpose of routine QA constancy tests. With minimum user interactions a comprehensive set of tests can be performed efficiently, allowing frequent monitoring of the Linac. The presenting author's salary is funded by the manufacturer of the QA device. All the other authors have financial interests with the commercialization of this QA device.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.317
Teacher spread0.289 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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