TU‐A‐BRB‐01: Groundbreaking 2D and 3D Dosimetry Techniques Using Scintillating Fibers and Tomographic Reconstruction
Bibliographic record
Abstract
Purpose: To present the novel concept of tomodosimetry, i.e. the tomographic reconstruction of the dose projections obtained using long scintillating fibers, and its application to 2D and 3D dosimetry. Methods: 2D configuration: 50 scintillating fibers were aligned on a 20cm diameter disk inside a 30cm diameter rotating masonite phantom. 18 dose projections (8 MU each) were measured for each radiation field over a 180 degrees rotation of the phantom. 3D configuration: 128 long scintillating fibers were simulated inside a 20cm diameter, 20cm long cylindrical water‐equivalent phantom. The fibers were placed at various angles on the surface of two cylindrical regions of radius 7.5 and 3.75cm. Using the predicted dose from Pinnacle3, we simulated a 360 degrees rotation of the phantom along its principal axis, collecting the scintillation light from the fibers at each 5 degrees. Both prototypes: the dose in each scintillating fiber plane was reconstructed using a total variation minimization reconstruction iterative algorithm at a resolution of 1×1mm2, and was interpolated in the 3D volume between in each cylindrical plane in the 3D prototype. Results: Absolute measured dose differences in the 2D configuration were on average below 1% in the high dose low gradient region of each field. Absolute doses differences calculated inside the inner cylindrical region were on average of 0.5% and 1.3% of the isocenter dose for a 10×10cm2 field and an IMRT segment, respectively. 3%/3mm gamma tests conducted in both configurations in the isocenter plane achieved a success rate of more than 99% of the dose pixels for the region over 50% of the maximum dose. Conclusions: This work demonstrates the potential of scintillating fiber based tomographic 2D and 3D dosimeters. This methodology allows for millimeter resolution dosimetry in a whole 2D plane or 3D volumes in realtime 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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".