Characterisations of a fibre optic dosimetry system for source tracking during HDR Brachytherapy
Bibliographic record
Abstract
Brachytherapy is a complex treatment procedure where radioactive sources are inserted in or close to the tumours to destroy the cancerous cells. Due to the unique properties of scintillation plastic detectors, this study was aimed to characterise an innovative fibre optic dosimetry system as a quality assurance tool during HDR Brachytherapy. Scintillating plastic fibres with different scintillation lengths were prepared and then optically coupled to non-scintillating fibres for light transmission. A transimpedance photodiode amplifier was used to detect positional sensitivities of different fibre probes placed within a solid-water phantom at varying distances above an 192-Ir brachytherapy source located within the catheter. Monte Carlo simulation has validated the expected response of the scintillating plastic fibres for multiple dwell positions with demonstrating the variance of detector response with source location. It showed the ability of shorter scintillating fibre lengths to distinguish between varying source locations when the SNR maintained high. However, fully scintillating plastic fibre showed flat response for most dwell positions. The proposed system proved to be appropriate for further clinical investigations, such as simultaneous dose measurement, and providing 3D position reconstruction through in-vivo source tracking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".