MétaCan
Menu
Back to cohort
Record W2883163141 · doi:10.1002/mp.13498

Optimization of a multipoint plastic scintillator dosimeter for high dose rate brachytherapy

2019· article· en· W2883163141 on OpenAlexafffund

Bibliographic record

VenueMedical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Texas MD Anderson Cancer CenterCanada Foundation for Innovation
KeywordsDosimeterBrachytherapyScintillatorDose rateDosimetryDose profile

Abstract

fetched live from OpenAlex

Purpose This study is devoted to optimizing and characterizing the response of a multipoint plastic scintillator detector (mPSD) for application to in vivo dosimetry in high dose rate (HDR) brachytherapy. Methods An exhaustive analysis was carried out in order to obtain an optimized mPSD design that maximizes the scintillation light collection produced by the interaction of ionizing photons. More than 20 prototypes of mPSD were built and tested in order to determine the appropriate order of scintillators relative to the photodetector (distal, center, or proximal) as well as their length as a function of the scintillation light emitted. The available detecting elements are the BCF‐60, BCF‐12, and BCF‐10 scintillators (Saint Gobain Crystals, Hiram, OH, USA), separated from each other by segments of Eska GH‐4001 clear optical fibers (Mitsubishi Rayon Co., Ltd., Tokyo, Japan). The contribution of each scintillator to the total spectrum was determined by irradiations in the low energy range (<120 keV). For the best mPSD design, a numerical optimization was done in order to select the optical components [dichroic mirrors, filters, and photomultipliers tubes (PMTs)] that best match the light emission profile. Calculations were performed taking into account the measured scintillation spectrum and light yield, the manufacturer‐reported transmission and attenuation of the optical components, and the experimentally characterized PMT noise. The optimized dosimetric system was used for HDR brachytherapy measurements. The system was independently controlled from the Ir source via LabVIEW and read simultaneously using an NI‐DAQ board. Dose measurements as a function of distance from the source were carried out according to TG‐43U1 recommendations. The system performance was quantified in terms of signal to noise ratio (SNR) and signal to background ratio (SBR). Results For best overall light‐yield emission, it was determined that BCF‐60 should be placed at the distal position, BCF‐12 in the center, and BCF‐10 at the proximal position with respect to the photodetector. This configuration allowed for optimized light transmission through the collecting fiber and avoided inter‐scintillator excitation and self‐absorption effects. The optimal scintillator length found was of 3, 6, and 7 mm for BCF‐10, BCF‐ 12, and BCF‐60, respectively. The optimized luminescence system allowed for signal deconvolution using a multispectral approach, extracting the dose to each element while taking into account the Cerenkov stem effect. Differences between the mPSD measurements and TG‐43U1 remain below 5% in the range of 0.5 to 6.5 cm from the source. The dosimetric system can properly differentiate the scintillation signal from the background for a wide range of dose rate conditions; the SNR was found to be above 5 for dose rates above 22 mGy/s while the minimum SBR measured was 1.8 at 6 mGy/s. Conclusion Based on the spectral response at different conditions, an mPSD was constructed and optimized for HDR brachytherapy dosimetry. It is sensitive enough to allow multiple simultaneous measurements over a clinically useful distance range, up to 6.5 cm from the source. This study constitutes a baseline for future applications enabling real‐time dose measurements and source position reporting over a wide range of dose rate conditions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.852

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)0.0010.000

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations29
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207