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Record W2995870186 · doi:10.1002/mp.13968

Absolute dosimetry of a 1.5 T MR‐guided accelerator‐based high‐energy photon beam in water and solid phantoms using Aerrow

2019· article· en· W2995870186 on OpenAlexaff
James Renaud, Arman Sarfehnia, Julien Bancheri, Jan Seuntjens

Bibliographic record

VenueMedical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of TorontoMcGill UniversityNational Research Council Canada
Fundersnot available
KeywordsDosimetryDosimeterIonization chamberImaging phantomMonte Carlo methodPhotonMaterials scienceLinear particle acceleratorDose profileDetectorAbsorbed doseCalorimeter (particle physics)Medical physicsPhysicsBeam (structure)Nuclear medicineOpticsIonizationMedicineMathematics

Abstract

fetched live from OpenAlex

PURPOSE: In this work, the fabrication, operation, and evaluation of a probe-format graphite calorimeter - herein referred to as Aerrow - as an absolute clinical dosimeter of high-energy photon beams while in the presence of a B = 1.5 T magnetic field is described. Comparable to a cylindrical ionization chamber (IC) in terms of utility and usability, Aerrow has been developed for the purpose of accurately measuring absorbed dose to water in the clinic with a minimum disruption to the existing clinical workflow. To our knowledge, this is the first reported application of graphite calorimetry to magnetic resonance imaging (MRI)-guided radiotherapy. METHODS: Based on a previously numerically optimized and experimentally validated design, an Aerrow prototype capable of isothermal operation was constructed in-house. Graphite-to-water dose conversions as well as magnetic field perturbation factors were calculated using Monte Carlo, while heat transfer and mass impurity corrections and uncertainties were assessed analytically. Reference dose measurements were performed in the absence and presence of a B = 1.5 T magnetic field using Aerrow in the 7 MV FFF photon beam of an Elekta MRI-linac and were directly compared to the results obtained using two calibrated reference-class IC types. The feasibility of performing solid phantom-based dosimetry with Aerrow and the possible influence of clearance gaps is also investigated by performing reference-type dosimetry measurements for multiple rotational positions of the detector and comparing the results to those obtained in water. RESULTS: In the absence of the B-field, as well as in the parallel orientation while in the presence of the B-field, the absorbed dose to water measured using Aerrow was found to agree within combined uncertainties with those derived from TG-51 using calibrated reference-class ICs. Statistically significant differences on the order of (2-4)%, however, were observed when measuring absorbed dose to water using the ICs in the perpendicular orientation in the presence of the B-field. Aerrow had a peak-to-peak response of about 0.5% when rotated within the solid phantom regardless of whether the B-field was present or not. CONCLUSIONS: This work describes the successful use of Aerrow as a straightforward means of measuring absolute dose to water for large high-energy photon fields in the presence of a 1.5 T B-field to a greater accuracy than currently achievable with ICs. The detector-phantom air gap does not appear to significantly influence the response of Aerrow in absolute terms, nor does it contribute to its rotational dependence. This work suggests that the accurate use of solid phantoms for absolute point dose measurement is possible with Aerrow.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.288
Teacher spread0.276 · 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".

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Citations11
Published2019
Admission routes1
Has abstractyes

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