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Record W2076060472 · doi:10.1118/1.3572229

A phantom study of an <i>in vivo</i> dosimetry system using plastic scintillation detectors for real-time verification of <sup>192</sup>Ir HDR brachytherapy

2011· article· en· W2076060472 on OpenAlexaff
François Therriault‐Proulx, Tina M. Briere, Firas Mourtada, Sylviane Aubin, Sam Beddar, Luc Beaulieu

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCentre hospitalier universitaire de QuébecHôtel-Dieu de QuébecUniversité Laval
FundersNational Cancer InstituteUniversity of Texas MD Anderson Cancer Center
KeywordsImaging phantomDosimetryBrachytherapyScintillationDose profileNuclear medicineRadiation treatment planningMaterials scienceBiomedical engineeringDetectorMedical physicsOpticsMedicinePhysicsRadiation therapyRadiology

Abstract

fetched live from OpenAlex

PURPOSE: The goal of the present work was to evaluate the accuracy of a plastic scintillation detector (PSD) system to perform in-phantom dosimetry during 192Ir high dose rate (HDR) brachytherapy treatments. METHODS: A PSD system capable of stem effect removal was built. A red-green-blue photodiode connected to a dual-channel electrometer was used to detect the scintillation light emitted from a green scintillation component and transmitted along a plastic optical fiber. A clinically relevant prostate treatment plan was built using the HDR brachytherapy treatment planning system. An in-house fabricated template was used for accurate positioning of the catheters, and treatment delivery was performed in a water phantom. Eleven catheters were inserted and used for dose delivery from 192Ir radioactive source, while two others were used to mimic dosimetry at the rectum wall and in the urethra using a PSD. The measured dose and dose rate data were compared to the expected values from the planning system. The importance of removing stem effects from in vivo dosimetry using a PSD during 192Ir HDR brachytherapy treatments was assessed. Applications for dwell position error detection and temporal verification of the treatment delivery were also investigated. RESULTS: In-phantom dosimetry measurements of the treatment plan led to a ratio to the expected dose of 1.003 +/- 0.004 with the PSD at different positions in the urethra and 1.043 +/- 0.003 with the PSD inserted in the rectum. Verification for the urethra of dose delivered within each catheter and at specific dwell positions led to average measured to expected ratios of 1.015 +/- 0.019 and 1.014 +/- 0.020, respectively. These values at the rectum wall were 1.059 +/- 0.045 within each catheter and 1.025 +/- 0.028 for specific dwell positions. The ability to detect positioning errors of the source depended of the tolerance on the difference to the expected value. A 5-mm displacement of the source was detected by the PSD system from 78% to 100% of the time depending on the acceptable range value. The implementation of a stem effect removal technique was shown to be necessary, particularly when calculating doses at specific dwell positions, and allowed decreasing the number of false-error detections-the detection of an error when it should not be the case--from 19 to 1 for a 5% threshold out of 43 measurements. The use of the PSD system to perform temporal verification of elapsed time by the source in each catheter--generally on the order of minutes--was shown to be in agreement within a couple of seconds with the treatment plan CONCLUSIONS: We showed that the PSD system used in this study, which was capable of stem effect removal, can perform accurate dosimetry during 192Ir HDR brachytherapy treatment in a water phantom. The system presented here shows some clear advantages over previously proposed dosimetry systems for HDR brachytherapy, and it has the potential for various online verifications of treatment delivery quality.

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 score0.400
Threshold uncertainty score0.678

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 head0.020
GPT teacher head0.290
Teacher spread0.271 · 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

Citations86
Published2011
Admission routes1
Has abstractyes

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