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Record W2942777861 · doi:10.1002/mp.13414

Cherenkov emission‐based external radiotherapy dosimetry: I. Formalism and feasibility

2019· article· en· W2942777861 on OpenAlexafffund
Yana Zlateva, Bryan Muir, Issam El Naqa, Jan Seuntjens

Bibliographic record

VenueMedical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council CanadaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesMcGill University Health CentreNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDosimetryPhysicsDetectorCherenkov radiationDosimeterTruebeamMonte Carlo methodElectronOpticsComputational physicsPhotonCharged particleFormalism (music)Beam (structure)RadiationNuclear physicsLinear particle acceleratorNuclear medicineIon

Abstract

fetched live from OpenAlex

Purpose Cherenkov emission (CE)‐based external beam dosimetry is envisioned to involve the detection of CE directly in water with placement of a high‐resolution detector out of the field, avoiding perturbations encountered with traditional dosimeters. In this work, we lay out the groundwork for its implementation in the clinic and motivate CE‐based dosimeter design efforts. To that end, we examine a formalism for broad‐beam in‐water CE‐based dosimetry of external radiotherapy beams, design and test a Monte Carlo (MC) simulation framework for the calculation of CE‐to‐dose conversion factors used by the formalism, and demonstrate the experimental feasibility of this method. Methods The formalism is conceptually analogous to ionization‐based dosimetry and employs CE‐to‐dose conversion factors, , including only and all CE generated within polar angles θ ± δ θ on beam axis. The EGSnrc user code SPRRZnrc is modified to calculate , as well as CE spectral and angular distributions. The modified code is tested with monoenergetic parallel electrons on a thin water slab. Detector configurations are examined for broad 6–22 MeV electron beams from a BEAMnrc TrueBeam model, with a focus on (4π detection), , and ( is the CE angle of relativistic electrons in water). We perform a relative experimental validation at with electron beams, using a simple detector design with spherical optics and geometrical optics approximation of the sensitive volume, which spans the water tank. Due to transient charged particle equilibrium, broad photon beams are generally less sensitive to beam quality, depth, and angle. Results For 0.1–50 MeV electrons on a thin water slab, the code outputs CE photon spectral density per unit mass (calculated from dose and ) and angle in agreement with theory within ±0.03% and , respectively, corresponding to the output precision. The configuration was found impractical due to detection considerations. Detection at for small δ θ exhibited beam quality dependence of the same order as well as strong superficial depth dependence. A 4π configuration ameliorates these effects. A more practical approach may employ a large numerical aperture. In comparing with literature, we find that these effects are less pronounced for broad photon beams in water, as expected. Measured relative at small δ θ were within 1% of simulated factors (relative to their local average) for percent‐depth CE (PDC) >50%. At other depths, deviations were in accordance with signal‐to‐noise, known detector limitations, and approximations. It was found that the CE spectrum is beam quality and depth invariant, while for electron beams the CE angular distribution is strongly dependent on beam quality and depth. However, the uncertainty of CE and PDC measurement at detection for small δ θ due to deviations around δ θ was shown to be ≤1% and <0.1% ( k = 1), respectively. The robustness to expected detector setup variations was found to result in ≤1% ( k = 1) local uncertainty contribution for PDC >50%. Conclusions Based on our MC and experimental studies, we conclude that the CE‐based method is promising for high‐resolution, perturbation‐free, three‐dimensional dosimetry in water, with specific applications contingent on comprehensive detector development and characterization.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.306
Teacher spread0.293 · 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 teacher head, not a consensus.

Study designObservational
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

Citations23
Published2019
Admission routes2
Has abstractyes

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