SU‐F‐BRE‐07: Experimental Validation of a Lung SBRT Technique Using a Novel, True Volumetric Plenoptic‐Plastic‐Scintillator Detector
Bibliographic record
Abstract
Purpose: Lung SBRT is being used by an increasing number of clinics, including our center which recently treated its first patient. In order to validate this technique, the 3D dose distribution of the SBRT plan was measured using a previously developed 3D detector based on plenoptic camera and plastic scintillator technology. The excellent agreement between the detector measurement and the expected dose from the treatment planning system Pinnacle3 shows great promise and amply justify the development of the technique. Methods: The SBRT treatment comprised 8 non‐coplanar 6MV photon fields with a mean field size of 12 cm2 at isocentre and a total prescription dose of 12Gy per fraction for a total of 48Gy. The 3D detector was composed of a 10×10×10 cm2 EJ‐260 water‐equivalent plastic scintillator embedded inside a truncated cylindrical acrylic phantom of 10cm radius. The scintillation light was recorded using a static R5 light‐field camera and the 3D dose was reconstructed at a 2mm resolution in all 3 dimensions using an iterative backprojection algorithm. Results: The whole 3D dose distribution was recorded at a rate of one acquisition per second. The mean absolute dose difference between the detector and Pinnacle3 was 1.3% over the region with more than 10% of the maximum dose. 3D gamma tests performed over the same region yield passing rates of 98.8% and 96.6% with criteria of 3%/1mm and 2%/1mm, respectively. Conclusion: Experimental results showed that our beam modeling and treatment planning system calculation was adequate for the safe administration of small field/high dose techniques such as SBRT. Moreover, because of the real‐time capability of the detector, further validation of small field rotational, dynamic or gated technique can be monitored or verified by this system.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".