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Record W2037722172 · doi:10.1118/1.2962057

SU‐GG‐T‐305: Feasibility of Using a Programmable Respiratory Motion Phantom for QA and Assessment of Dosimetric Implications of Breathing Motion During Radiation Therapy

2008· article· en· W2037722172 on OpenAlexaff
Julia Publicover, Aaron Vandermeer, B Norrlinger, Hamideh Alasti

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImaging phantomBreathingReproducibilityQuality assuranceDosimetryNuclear medicineComputer scienceMedical imagingRadiation treatment planningBiomedical engineeringMedical physicsPhysicsRadiation therapyMedicineArtificial intelligenceMathematicsRadiologyStatistics

Abstract

fetched live from OpenAlex

Purpose: Respiratory motion introduces uncertainties during CT and radiation therapy delivery. Reliable equipment and quality assurance (QA) techniques must be established to assess and overcome these uncertainties. Our goal was to validate the performance of a programmable motion phantom for QA and to demonstrate the dosimetric impact of breathing motion on treatment delivery. Method and Materials: The “Quasar” phantom (Modus Medical, London, ON) was assessed for suitability in QA procedures for radiation therapy involving respiratory motion. The phantom is equipped with a programmable unit, which introduces motion to cylindrical lung inserts. We tested the standard mode of motion and the “oscillation” mode, in which patient breathing profiles are imported and reproduced. Phantom motion reproducibility and accuracy were assessed using the Varian Real‐time Position Management (RPM) system and video for the extreme breathing periods (1 and 15seconds) and a patient representative breathing period of 4seconds. An in‐house designed cedar lung insert was built containing a target (4cm by 7cm). Film is placed in the insert to assess the dose distribution under phantom motion from static and dynamic delivery under phantom motion. The dynamic MLC treatment delivery was synchronized with target motion. Results: Using the RPM system, percent differences between the intended and actual periods for each were 0.57%, −1.70% and −0.22% respectively. When the amplitude was changed from 2cm to 1cm, the measured period did not change. Comparison of the breathing profiles in oscillation mode with profiles generated using the RPM system shows a close correspondence, with slight divergence at extreme direction or speed variation. Dose distribution for a phantom motion of 2cm peak‐to‐peak and period of 3.2sec along the moving direction indicates significant broadening of (80–20%) penumbra for static delivery (1.67cm) compared to dynamic delivery (0.80cm) Conclusion: The Quasar phantom is suitable for QA and dosimetric measurements of moving targets.

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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.377
Teacher spread0.322 · 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
GenreMethods

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

Citations3
Published2008
Admission routes1
Has abstractyes

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