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Record W2981859588 · doi:10.1002/mp.14246

Dosimetric performance of a multipoint plastic scintillator dosimeter as a tool for real‐time source tracking in high dose rate Ir brachytherapy

2020· article· en· W2981859588 on OpenAlexafffund
Haydee M. Linares Rosales, Louis Archambault, Sam Beddar, Luc Beaulieu

Bibliographic record

VenueMedical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScintillatorDosimeterScintillationOpticsPhotomultiplierDetectorBrachytherapyPhysicsDosimetryDose profilePoint sourceStandard deviationTracking (education)Radioactive sourceNuclear medicineMaterials scienceRadiationImaging phantomMathematicsStatistics

Abstract

fetched live from OpenAlex

Purpose This study aims to present the performance of a multipoint plastic scintillation detector (mPSD) as a tool for real‐time dose measurements (covering three orders of magnitude in dose rate), source‐position triangulation, and dwell time assessment in high dose rate (HDR) brachytherapy. Methods A previously characterized and optimized three‐point sensor system was used for HDR brachytherapy measurements. The detector was composed of three scintillators: BCF60, BCF12, and BCF10. Scintillation light was transmitted through a single 1‐mm‐diameter clear optical fiber and read by a compact assembly of photomultiplier tubes (PMTs). Each component was numerically optimized to allow for signal deconvolution using a multispectral approach, taking care of the Cerenkov stem effect as well as extracting the dose from each scintillator. The PMTs were read simultaneously using a data acquisition board at a rate of 100 KHz and controlled with in‐house software based on Python. An source (Flexitron, Elekta‐Brachy) was remotely controlled and sent to various positions in a in‐house PMMA holder, ensuring 0.1 mm positional accuracy. Dose measurements covering a range of 10 cm of source movement were carried out according to TG‐43 U1 recommendations. Water measurements were performed in order to: (a) characterize the system’s response in terms of angular dependence; (b) obtain the relative contribution of positioning and measurement uncertainties to the total system uncertainty; (c) assess the system’s temporal resolution; and (d) track the source position in real time. The triangulation principle was applied to report the source position in three‐dimensional space. Results As expected, the positioning uncertainty dominated close to the source, whereas the measurement uncertainty dominated at larger distances. A maximum measurement uncertainty of 17 % was observed for the BCF60 scintillator at 10 cm from the source. Based on the uncertainty chain, the best compromises between positioning and measurement uncertainties were reached at 17.2, 17.4, and 17.5 mm for the BCF10, BCF12, and BCF60 scintillators, respectively, which also corresponded to the recommended optimal distances to the source for calibration purposes. The detector further exhibited no angular dependence. All dose values were found to be within 2% of the dose value at . In the experiments performed for source‐position determination, the system provided an average location with a standard deviation under 1.7 mm. The maximum observed differences between measured and expected values were 1.82 and 1.8 mm in the x‐ and z‐directions, respectively. Deviations between the mPSD measurements and expected TG‐43 values were below 5% in all the explored measurement conditions. With regard to dwell time measurement accuracy, the maximum deviation observed at all distances was 0.56 ± 0.25 s, with a weighted average of the three scintillators below 0.33 ± 0.37 s at all distances covered in this study. Conclusions Real‐time HDR brachytherapy measurements were performed with an optimized mPSD system. The performance of the system demonstrated that it could be used for simultaneous, in vivo, real‐time reporting of dose, dwell time, and source position during HDR brachytherapy.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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 designOther design
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".

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Citations27
Published2020
Admission routes2
Has abstractyes

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