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Record W2002669104 · doi:10.1109/ultsym.2014.0008

Simulation and experimental detection of radiation-induced acoustic waves from a radiotherapy linear accelerator

2014· article· en· W2002669104 on OpenAlexaff
Susannah Hickling, Pierre Léger, Issam El Naqa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsMcGill University
Fundersnot available
KeywordsLinear particle acceleratorDosimetryMonte Carlo methodPhysicsTransducerPhotonAcousticsSIGNAL (programming language)Beam (structure)OpticsComputer scienceNuclear medicine

Abstract

fetched live from OpenAlex

Acoustic waves are generated in objects irradiated with megavoltage photon beams produced by a clinical linear accelerator. The detection of such induced acoustic waves (IAWs) has potential applications in radiation therapy dosimetry. This work developed a novel simulation platform to model IAWs by combining radiotherapy Monte Carlo and acoustic wave transport simulation techniques. Simulations and experimental measurements were performed to assess the IAWs when a lead block suspended in a water tank was irradiated with a photon beam produced by a clinical linear accelerator. Simulations correctly predicted the frequency of such IAWs, which allowed improved experimental detection through the development of a custom band-pass filter and signal amplification system. Photon beam energy, lead block depth, and lead block distance from the transducer were varied, and experimental and simulated signals showed the same trends for such set-up changes. These results demonstrate the ability of our simulation platform to accurately predict the frequency spectrum and relative amplitude trends of IAWs. The simulation platform can be applied to assess IAWs in clinically relevant radiotherapy dosimetry situations.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.344

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)0.0000.000

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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.

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

Citations10
Published2014
Admission routes1
Has abstractyes

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