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Record W4392958280 · doi:10.3390/electronics13061118

A Multi-Point Optical Fibre Sensor for Proton Therapy

2024· article· en· W4392958280 on OpenAlexafffund
Crystal Penner, Samuel Usherovich, Sophia Andru, C. Bélanger-Champagne, Cheryl Duzenli, Boris Stoeber, Cornelia Hoehr

Bibliographic record

VenueElectronics · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersTRIUMF
KeywordsDosimetryScintillatorIonization chamberCalibrationBeam (structure)OpticsProton therapyProtonOptical fiberMaterials scienceNuclear medicineIonizationOptoelectronicsPhysicsDetectorNuclear physicsMedicine

Abstract

fetched live from OpenAlex

As the technology to deliver precise and very high radiotherapeutic doses with narrow margins grows to better serve patients with complex radiotherapeutic needs, so does the need for sensors and sensor systems that can reliably deliver multi-point dose monitoring and dosimetry for enhanced safety and access. To address this need, we investigated a novel five-point scintillator system for simultaneously sampling points across a 74 MeV proton beam with a Hamamatsu 16-channel MPPC array. We studied the response across beam widths from 25 mm down to 5 mm in diameter and in multiple depths to observe beam penumbrae and output factors as well as depth–dose. We found through comparison to ionization chambers and radiochromic film that the array is capable of measurements accurate to within 8% in the centre of proton beams from 5 to 25 mm in diameter, and within 2% at 3.5 cm depth in water. The results from three trials are repeatable after calibration to within <1%. Overall, the five optical fibre sensor system shows promise as a fast, multipoint relative dosimetry system.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.332

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 head0.021
GPT teacher head0.320
Teacher spread0.299 · 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 designNot applicable
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

Citations5
Published2024
Admission routes2
Has abstractyes

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