Deformable scintillation dosimeter I: challenges and implementation using computer vision techniques
Bibliographic record
Abstract
Plastic scintillation detectors are increasingly used to measure dose distributions in the context of radiotherapy treatments. Their water-equivalence, real-time response and high spatial resolution distinguish them from traditional detectors, especially in complex irradiation geometries. Their range of applications could be further extended by embedding scintillators in a deformable matrix mimicking anatomical changes. In this work, we characterized signal variations arising from the translation and rotation of scintillating fibers with respect to a camera. Corrections are proposed using stereo vision techniques and two sCMOS complementing a CCD camera. The study was extended to the case of a prototype real-time deformable dosimeter comprising an array of 19 scintillating fibers. The signal to angle relationship follows a gaussian distribution (FWHM = 52°) whereas the intensity variation from radial displacement follows the inverse square law. Tracking the position and angle of the fibers enabled the correction of these spatial dependencies. The detecting system provides an accuracy and precision of respectively 0.08 mm and 0.3 mm on the position detection. This resulted in an uncertainty of 2° on the angle measurement. Displacing the dosimeter by ±3 cm in depth resulted in relative intensities of 100 ± 10% (mean ± standard deviation) to the reference position. Applying corrections reduced the variations thus resulting in relative intensities of 100 ± 1%. Similarly, for lateral displacements of ±3 cm, intensities went from 98 ± 3% to 100 ± 1% after the correction. Therefore, accurate correction of the signal collected by a camera imaging the output of scintillating elements in a 3D volume is possible. This work paves the way to the development of real-time scintillator-based deformable dosimeters.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".