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Record W3159243216 · doi:10.1002/mp.15000

High spatial resolution dosimetry with uncertainty analysis using Raman micro‐spectroscopy readout of radiochromic films

2021· preprint· en· W3159243216 on OpenAlexafffund
Connor McNairn, Iymad Mansour, Bryan Muir, Rowan M. Thomson, Sangeeta Murugkar

Bibliographic record

VenueMedical Physics · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRaman spectroscopyDosimetryMaterials scienceCalibrationCalibration curveOpticsAnalytical Chemistry (journal)SpectroscopyImage resolutionResolution (logic)Nuclear medicineChemistryPhysicsDetection limitMedicine

Abstract

fetched live from OpenAlex

Purpose The purpose of this work is to develop a new approach for high spatial resolution dosimetry based on Raman micro‐spectroscopy scanning of radiochromic film (RCF). The goal is to generate dose calibration curves over an extended dose range from 0 to 50 Gy and with improved sensitivity to low (<2 Gy) doses, in addition to evaluating the uncertainties in dose estimation associated with the calibration curves. Methods Samples of RCF (EBT3) were irradiated at a broad dose range of 0.03–50 Gy using an Elekta Synergy clinical linear accelerator. Raman spectra were acquired with a custom‐built Raman micro‐spectroscopy setup involving a 500 mW, multimode 785 nm laser focused to a lateral spot diameter of 30 µm on the RCF. The depth of focus of 34 µm enabled the concurrent collection of Raman spectra from the RCF active layer and the polyester laminate. The preprocessed Raman spectra were normalized to the intensity of the 1614 cm−1 Raman peak from the polyester laminate that was unaltered by radiation. The mean intensities and the corresponding standard deviation of the active layer Raman peaks at 696, 1445, and 2060 cm−1 were determined for the 150 × 100 µm2 scan area per dose value. This was used to generate three calibration curves that enabled the conversion of the measured Raman intensity to dose values. The experimental, fitting, and total dose uncertainty was determined across the entire dose range for the dosimetry system of Raman micro‐spectroscopy and RCF. Results In contrast to previous work that investigated the Raman response of RCFs using different methods, high resolution in the dose response of the RCF, even down to 0.03 Gy, was obtained in this study. The dynamic range of the calibration curves based on all three Raman peaks in the RCF extended up to 50 Gy with no saturation. At a spatial resolution of 30 × 30 µm2, the total uncertainty in estimating dose in the 0.5–50 Gy dose range was [6–9]% for all three Raman calibration curves. This consisted of the experimental uncertainty of [5–8]%, and the fitting uncertainty of [2.5–4.5]%. The main contribution to the experimental uncertainty was determined to be from the scan area inhomogeneity which can be readily reduced in future experiments. The fitting uncertainty could be reduced by performing Raman measurements on RCF samples at further intermediate dose values in the high and low dose range. Conclusions The high spatial resolution experimental dosimetry technique based on Raman micro‐spectroscopy and RCF presented here, could become potentially useful for applications in microdosimetry to produce meaningful dose estimates in cellular targets, as well as for applications based on small field dosimetry that involve high dose gradients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.287
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes2
Has abstractyes

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