MO‐F‐214‐02: A New, High Resolution 2D Dose Measurement Device Based on a Few Scintillating Fibers and Tomographic Reconstruction
Bibliographic record
Abstract
Purpose: To develop and validate a novel type of 2D dosimeter based on the tomographic reconstruction of the dose projections obtained using long scintillating fibers. Methods: 50 parallel scintillating fibers (diameter, 1mm; length, 6 to 20cm) were aligned in a 30cm diameter cylindrical masonite phantom with a 90cm source‐to‐surface distance and a 100cm source‐to‐fibers distance. The fibers were disposed so that the effective detection area of the scintillating fibers was a 20cm diameter disk. Both ends of each scintillating fiber were coupled to clear optical fibers to enable light collection by a single CCD camera using an f/2, 50mm focal length lens. 7 IMRT segments and 2 square fields were acquired using 18 projections over a 170 degrees rotation of the device. Dose reconstructions were conducted using a total‐variation minimization reconstruction algorithm. 8 monitoring units were programmed for each projection and the reconstructed dose grid pixel resolution was set to 1×1mm,2,. Results: Using a non‐optimized algorithm on a 2GHz CPU, each reconstruction was performed in less than 6 minutes. 3%/3mm gamma tests conducted between the reconstructed IMRT dose distributions and the dose calculated with the treatment planning system Pinnacle,3, were on average successful for 99.6% of the dose pixels for the region over 10% of the maximum dose. For both square fields and the whole summed IMRT field, 100% of the dose pixels were successful to the gamma test. Conclusions: Using tomographic reconstruction on the projections acquired with rotating scintillating fibers, one is able to perform 2D dosimetry of simple and IMRT fields with great accuracy and resolution using only a limited number of scintillating fibers. The underlying concept of tomographic dosimetry and the small number of fibers needed to reconstruct a given 2D dose distribution offer a world of new dosimetric possibility, both applicable to 2D and 3D dosimetry.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".