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Record W2277362360 · doi:10.1109/tuffc.2016.2528960

On the Detectability of Acoustic Waves Induced Following Irradiation by a Radiotherapy Linear Accelerator

2016· article· en· W2277362360 on OpenAlexafffund
Susannah Hickling, Pierre Léger, Issam El Naqa

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health Research
KeywordsLinear particle acceleratorDosimetryAcousticsMonte Carlo methodOpticsBeam (structure)RadiationAmplitudePhysicsMaterials scienceNuclear medicine

Abstract

fetched live from OpenAlex

Irradiating an object with a megavoltage photon beam generated by a clinical radiotherapy linear accelerator (linac) induces acoustic waves through the photoacoustic effect. The detection and characterization of such acoustic waves has potential applications in radiation therapy dosimetry. The purpose of this work was to gain insight into the properties of such acoustic waves by simulating and experimentally detecting them in a well-defined system consisting of a metal block suspended in a water tank. A novel simulation workflow was developed by combining radiotherapy Monte Carlo and acoustic wave transport simulation techniques. Different set-up parameters such as photon beam energy, metal block depth, metal block width, and metal block material were varied, and the simulated and experimental acoustic waveforms showed the same relative amplitude trends and frequency variations for such setup changes. The simulation platform developed in this work can easily be extended to other irradiation situations, and will be an invaluable tool for developing a radiotherapy dosimetry system based on the detection of the acoustic waves induced following linear accelerator irradiation.

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: none
Teacher disagreement score0.572
Threshold uncertainty score0.784

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 head0.008
GPT teacher head0.205
Teacher spread0.196 · 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

Citations30
Published2016
Admission routes2
Has abstractyes

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