WE‐G‐BRB‐07: Application of RADPOS in Vivo Dosimetry for QA of High Dose Rate Brachytherapy
Bibliographic record
Abstract
PURPOSE: The RADPOS in vivo dosimetry system combines an electromagnetic positioning sensor with MOSFET dosimetry, allowing for simultaneous online measurements of dose and spatial position. In this work, we assess the potential use of RADPOS for measurements of motion and dose during prostate HDR brachytherapy. METHODS: The RADPOS was positioned in the centre of a thin plastic tube supported by two parallel acrylic plates submerged in water. The detector was calibrated by sending an Ir-192 source into an adjacent tube, to positions ranging from 8.0 to -8.0 cm along the vertical axis in increments of 0.5 cm. The dwell time at each position was 20 s and the change in threshold voltage of the RADPOS dosimeter was recorded for each position. The expected dose for each source position was calculated and compared to RADPOS measurements to obtain a detector calibration coefficient (cGy/mV). The procedure was then repeated sending the Ir-192 source into 5 other tubes, located 1 to 10 cm away from the RADPOS. Source positions, dwell times, and position of the RADPOS detector were the same as for the calibration. The dose for each source position was determined by RADPOS measurements and calibration coefficient and compared to the expected dose. RESULTS: An average calibration coefficient of 0.74±0.11 cGy/mV was calculated for RADPOS measurements of Ir-192 irradiations. The absolute difference between RADPOS values and expected dose for subsequent measurements averaged over all source positions was 0.7±5.4 cGy. CONCLUSIONS: In vivo dosimetry can potentially signal errors in catheter placement or numbering before entire dose is delivered. The demonstrated accuracy of RADPOS dose measurements and its ability to simultaneously measure displacement makes it a powerful tool for HDR brachytherapy treatments for prostate cancer, where high dose gradients and movement of the prostate gland can present unique in vivo dosimetry challenges. Financial and technical support has been received from Best Medical Canada and Ascension Technology Corporation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".