Simulation and measurements of HDR brachytherapy source dosimetric parameters using a fiber-optic dosimeter
Bibliographic record
Abstract
The aim of this study is to evaluate the feasibility of using CsI(Tl)-based fibre-optic dosimetry system for TG-43 dosimetric characterization of the Flexisource 192 Ir HDR brachytherapy source. The investigation focuses on anisotropy and radial dose function measurements and assesses the impact of fluorescence and Cherenkov radiation on signal accuracy, with validation through TOPAS Monte Carlo simulations. Thallium Doped-Caesium Iodide (CsI(Tl)) scintillation crystals were used within a custom-designed PMMA phantom. The experiment, conducted with a Flexitron afterloader and 192 Ir Flexisource, involved measurements at distances of 1.5–6 cm and polar angles of 20°–155°, with increments matching those in the published studies. Comprehensive dosimetric data were collected, revealing the influence of fluorescence and Cherenkov radiation on anisotropy function measurements. The radial dose function showed good agreement with simulation, with minor deviations attributed to limitations in the treatment planning. TOPAS Monte Carlo simulations demonstrated consistent agreement with experimental results, yielding a maximum absolute difference of 0.035 in the experimental data and a maximum deviation of 2.6% in anisotropy function validation against published reference data, further confirming the reliability of both the experimental approach and the simulation model. These findings underscore the importance of accounting for fluorescence and Cherenkov radiation in detector signal response. Unlike earlier approaches that primarily focused on signal removal or hardware suppression techniques, this study demonstrates the integration of these contributions directly into calibration models to improve dosimetric precision. By refining these calibration methods, fibre-optic detectors may be further developed into simple, accurate, and clinically viable tools for brachytherapy applications.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".