Sci-Thur PM: YIS - 05: Tomographic dosimetry using scintillating fibers and its application to 2D and 3D dosimetry
Bibliographic record
Abstract
Purpose: To present the proof of concept and the experimental validation of tomographic dosimetry (tomodosimetry), where a tomographic acquisition of the incident deposited dose is performed using long scintillating fibers. Method: 2D tomodosimetry: 50 long scintillating fibers were aligned on a 20cm diameter disk inside a 30cm diameter masonite phantom. 3D tomodosimetry: 128 long scintillating fibers of various orientation were simulated on the surface of two cylindrical regions of radius 7.5 and 3.75cm inside a 20cm diameter, 20cm long cylindrical phantom. In both case, the dose projections were acquire each 5 degrees over a 180 degrees (2D) or 360 degrees (3D) rotation of the device, and the dose in each scintillating fiber plane was reconstructed using a total variation minimization reconstruction iterative algorithm at a resolution of 1×1mm2. The 3D dose was obtained by interpolating between in each cylindrical plane in the 3D prototype. Results: 3%/3mm gamma tests conducted in the isocentre plane for both configurations achieved a success rate of more than 99% of the dose pixels in the region over 50% of the maximum dose. Absolute dose differences in the high dose low gradient region of each scintillating fiber plane were on average below 1% for the 2D configuration and below 1.3% for the 3D configuration. Conclusions: This work illustrates the potential and capacity of scintillating fiber based 2D and 3D tomodosimeters. The presented methodology allows for millimeter resolution dosimetry in a whole 2D plane or 3D volumes in real-time using only a limited number of detectors.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".