SU‐C‐201‐04: Noise and Temporal Resolution in a Near Real‐Time 3D Dosimeter
Bibliographic record
Abstract
Purpose: To characterize the performance of a real‐time three‐dimensional scintillation dosimeter in terms of signal‐to‐noise ratio (SNR) and temporal resolution of 3D dose measurements. This study quantifies its efficiency in measuring low dose levels characteristic of EBRT dynamic treatments, and in reproducing field profiles for varying multileaf collimator (MLC) speeds. Methods: The dosimeter prototype uses a plenoptic camera to acquire continuous images of the light field emitted by a 10×10×10 cm3 plastic scintillator. Using EPID acquisitions, ray tracing‐based iterative tomographic algorithms allow millimeter‐sized reconstruction of relative 3D dose distributions. Measurements were taken at 6MV, 400 MU/min with the scintillator centered at the isocenter, first receiving doses from 1.4 to 30.6 cGy. Dynamic measurements were then performed by closing half of the MLCs at speeds of 0.67 to 2.5 cm/s, at 0° and 90° collimator angles. A reference static half‐field was obtained for measured profile comparison. Results: The SNR steadily increases as a function of dose and reaches a clinically adequate plateau of 80 at 10 cGy. Below this, the decrease in light collected and increase in pixel noise diminishes the SNR; nonetheless, the EPID acquisitions and the voxel correlation employed in the reconstruction algorithms result in suitable SNR values (>75) even at low doses. For dynamic measurements at varying MLC speeds, central relative dose profiles are characterized by gradients at %D50 of 8.48 to 22.7 %/mm. These values converge towards the 32.8 %/mm‐gradient measured for the static reference field profile, but are limited by the dosimeter's current acquisition rate of 1Hz. Conclusion: This study emphasizes the efficiency of the 3D dose distribution reconstructions, while identifying limits of the current prototype's temporal resolution in terms of dynamic EBRT parameters. This work paves the way for providing an optimized, second‐generational real‐time 3D scintillation dosimeter capable of highly efficient and precise dose measurements. The presenting author is financially supported by an Alexander‐Graham Bell doctoral scholarship from the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".