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Record W4389484972 · doi:10.1002/mp.16858

Experimental determination of magnetic field quality conversion factors for eleven ionization chambers in 1.5 T and 0.35 T MR‐linac systems

2023· article· en· W4389484972 on OpenAlexafffund
Nathan Orlando, Jennie Crosby, Carri Glide‐Hurst, Wesley S. Culberson, Brian Keller, Arman Sarfehnia

Bibliographic record

VenueMedical Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsIonization chamberCalibrationPhysicsIsocenterIonizationMagnetic fieldDosimetryMonte Carlo methodLinear particle acceleratorAtomic physicsOpticsNuclear medicineIonMedicineImaging phantomBeam (structure)

Abstract

fetched live from OpenAlex

Abstract Background The static magnetic field present in magnetic resonance (MR)‐guided radiotherapy systems can influence dose deposition and charged particle collection in air‐filled ionization chambers. Thus, accurately quantifying the effect of the magnetic field on ionization chamber response is critical for output calibration. Formalisms for reference dosimetry in a magnetic field have been proposed, whereby a magnetic field quality conversion factor kB,Q is defined to account for the combined effects of the magnetic field on the radiation detector. Determination of kB,Q in the literature has focused on Monte Carlo simulation studies, with experimental validation limited to only a few ionization chamber models. Purpose The purpose of this study is to experimentally measure kB,Q for 11 ionization chamber models in two commercially available MR‐guided radiotherapy systems: Elekta Unity and ViewRay MRIdian. Methods Eleven ionization chamber models were characterized in this study: Exradin A12, A12S, A28, and A26, PTW T31010, T31021, and T31022, and IBA FC23‐C, CC25, CC13, and CC08. The experimental method to measure kB,Q utilized cross‐calibration against a reference Exradin A1SL chamber. Absorbed dose to water was measured for the reference A1SL chamber positioned parallel to the magnetic field with its centroid placed at the machine isocenter at a depth of 10 cm in water for a 10 × 10 cm2 field size at that depth. Output was subsequently measured with the test chamber at the same point of measurement. kB,Q for the test chamber was computed as the ratio of reference dose to test chamber output, with this procedure repeated for each chamber in each MR‐guided radiotherapy system. For the high‐field 1.5 T Elekta Unity system, the dependence of kB,Q on the chamber orientation relative to the magnetic field was quantified by rotating the chamber about the machine isocenter. Results Measured kB,Q values for our test dataset of ionization chamber models ranged from 0.991 to 1.002, and 0.995 to 1.004 for the Elekta Unity and ViewRay MRIdian, respectively, with kB,Q tending to increase as the chamber sensitive volume increased. Measured kB,Q values largely agreed within uncertainty to published Monte Carlo simulation data and available experimental data. kB,Q deviation from unity was minimized for ionization chamber orientation parallel or antiparallel to the magnetic field, with increased deviations observed at perpendicular orientations. Overall (k = 1) uncertainty in the experimental determination of the magnetic field quality conversion factor, kB,Q was 0.71% and 0.72% for the Elekta Unity and ViewRay MRIdian systems, respectively. Conclusions For a high‐field MR‐linac, the characterization of ionization chamber performance as angular orientation varied relative to the magnetic field confirmed that the ideal orientation for output calibration is parallel. For most of these chamber models, this study represents the first experimental characterization of chamber performance in clinical MR‐linac beams. This is a critical step toward accurate output calibration for MR‐guided radiotherapy systems and the measured kB,Q values will be an important reference data source for forthcoming MR‐linac reference dosimetry protocols.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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