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Record W4229457546 · doi:10.1002/mp.15695

Monte Carlo optimization and experimental validation of a prototype ionization chamber for accurate magnetic resonance image guided radiation therapy (MRgRT) daily output constancy measurements in solid phantoms

2022· article· en· W4229457546 on OpenAlexaff
Bryan Muir, Humza Nusrat, Arman Sarfehnia, James Renaud

Bibliographic record

VenueMedical Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science CentreNational Research Council Canada
Fundersnot available
KeywordsIonization chamberImaging phantomAir gap (plumbing)Monte Carlo methodMaterials scienceLinear particle acceleratorIonizationMagnetic fieldDosimetryBrassOpticsPhysicsComputational physicsNuclear medicineBeam (structure)IonMathematics

Abstract

fetched live from OpenAlex

PURPOSE: To optimize the design, develop and test a prototype ionization chamber for accurate daily output constancy measurements in solid phantoms in clinical magnetic resonance-guided radiation therapy (MRgRT) radiotherapy beams. Up to 4% variations in response using commercial ionization chambers have been previously reported; the prototype ionization chamber developed here aims to minimize these variations. METHODS: Monte Carlo simulations with the EGSnrc code system are used to optimize an ionization chamber design by increasing the thickness of a brass (high-density, nonferromagnetic, easy-to-machine) wall until results consistent with no air gap are produced for simulations with a 1.5 T and 0.35 T magnetic field, with a 0.2 mm air gap and varying the placement of the chamber model within the air gap. Based on the results of these simulations, prototype ionization chambers are manufactured and tested in conventional linac beams and in a 7 MV Elekta Unity MR-linac. The chambers are rotated about their axes, both parallel and perpendicular to the 1.5 T magnetic field, through 360º in a plastic phantom with measurements made at each cardinal angle. This reveals any variation in chamber response by varying the thickness of the air gap between the chamber and the phantom. RESULTS: Monte Carlo simulations demonstrate that the optimal thickness of the chamber wall to mitigate the effect of an asymmetric air gap between the chamber and the plastic phantom is 1.1 mm of brass. With this thickness, the differences between simulations with and without an air gap and with asymmetric placement of the chamber within the air gap are less than 0.2%. A prototype chamber constructed with a 1.1 mm brass wall thickness exhibits less than 0.3% variation in response when rotated about its axis in the plastic phantom in a beam from an MR-linac, independent of whether its axis is parallel or perpendicular to the magnetic field. CONCLUSION: The optimized ionization chamber design and validated prototype for accurate MR-linac daily output constancy measurements allows utilization of conventional phantoms and procedures in MRgRT systems. This can minimize disruption to clinical workflow for MR-linac quality assurance measurements.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.318
Teacher spread0.291 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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