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Record W4399386715 · doi:10.3390/electronics13112211

Multi-Point Sensing via Organic Optical Fibres for FLASH Proton Therapy

2024· article· en· W4399386715 on OpenAlexafffund
Crystal Penner, Samuel Usherovich, Sophia Andru, C. Bélanger-Champagne, Janina Hohnholz, Boris Stoeber, Cheryl Duzenli, Cornelia Hoehr

Bibliographic record

VenueElectronics · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersTRIUMF
KeywordsFlash pointFlash (photography)ProtonMaterials scienceProton therapyPoint (geometry)OptoelectronicsOpticsComputer scienceChemistryPhysicsMathematicsOrganic chemistryGeometry

Abstract

fetched live from OpenAlex

Optical fibres are gaining popularity for relative dosimetry in proton therapy due to their spatial resolution and ability for near real-time acquisition. For FLASH proton therapy, these fibres need to handle higher dose rates and larger doses than for conventional proton dose rates. We developed a multi-point fibre sensor embedded in a 3D-printed phantom which can measure the profile of a FLASH proton beam. Seven PMMA fibres of 1 mm diameter were embedded in a custom 3D-printed plastic phantom of the same density as the fibres. The phantom was placed in a proton beam with FLASH dose rates at the TRIUMF Proton Therapy Research Centre (PTRC). The sensor was exposed to different proton energies, 13.5 MeV, 19 MeV and 40.4 MeV, achieved by adding PMMA bolus in front of the phantom and three different beam currents, varying the dose rates from 7.5 to 101 Gy/s. The array was able to record beam profiles in both transverse and axial directions in relative agreement with measurements from EBT-XD radiochromic films (transverse) and Monte Carlo simulations (axial). A decrease in light output over time was observed, which might be caused by radiation damage in the matrix of the fibre and characterised by an exponential decay function.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.427

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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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