Robust External-Beam Calibration of Plastic Scintillation Detectors for In-Vivo Dosimetry in HDR Brachytherapy
Bibliographic record
Abstract
Purpose: HDR brachytherapy is a widely adopted modality for cancer treatment. However, it is not free from error and uncertainty. In-vivo dosimetry (IVD) is the only technique that can confirm correct dose delivery. This study details and validates a calibration method for Plastic Scintillation Detector (PSD), bypassing dose gradient and positioning issues in brachytherapy calibration. Methods: The PRB-0057 PSD (Medscint, Canada) was calibrated, 1x1 mm scintillating fiber coupled to a 20 m Eska GH-4001 clear optical fiber (Mitsubishi Rayon, Japan). The fiber is connected to the Hyperscint-RP200 research platform for signal collection. Hyperspectral calibration was performed at a LINAC with a 6 MV beam, enabling removal of stem effects before brachytherapy measurements. For validation, an Iridium-192 Flexisource (Elekta Brachy, The Netherlands), Sk=29447U, was used in a motorized IBA-Blue-Phantom2 water tank (48x48x41cm3). Dose rates were measured at 10 Hz along the source z-axis at a fixed transverse distance of 1.2+/-0.05 cm in 0.2 cm steps. Relative difference (RD) between measured and TG-43U1 dose rates was assessed. A detailed uncertainty budget was associated with brachytherapy measurements. Results: Comparison shows good agreement with RD around 2.5 $\%$ at 1.2 cm, corresponding to positional uncertainties of <0.15 mm. At greater depths up to 8 cm, RDs increase to about 5 $\%$, mainly due to reduced light yield. Uncertainties depend on the source-detector distance, ranging from 3.81 to 6.39 $\%$ (k=1) over the explored range. Conclusions: Results confirm the PSD calibration effectiveness using a 6MV external beam with hyperspectral technique. Uncertainties close to the source align with positional errors and are dominated by reduced PSD sensitivity at larger distances. The study underlined the intrinsic limitation of IVD in the face of known uncertainties.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".