Scanning laser optical computed tomography system for large volume 3D dosimetry
Bibliographic record
Abstract
Stray light causes artifacts in optical computed tomography (CT) that negatively affect the accuracy of radiation dosimetry in gels or solids. Scatter effects are exacerbated by a large dosimeter volume, which is desirable for direct verification of modern radiotherapy treatment plans such as multiple-isocenter radiosurgery. The goal in this study was to design and characterize an optical CT system that achieves high accuracy primary transmission measurements through effective stray light rejection, while maintaining sufficient scan speed for practical application. We present an optical imaging platform that uses a galvanometer mirror for horizontal scanning, and a translation stage for vertical movement of a laser beam and small area detector for minimal stray light production and acceptance. This is coupled with a custom lens-shaped optical CT aquarium for parallel ray sampling of projections. The scanner images 15 cm diameter, 12 cm height cylindrical volumes at 0.33 mm resolution in approximately 30 min. Attenuation coefficients reconstructed from CT scans agreed with independent cuvette measurements within 2% for both absorbing and scattering solutions as well as small 1.25 cm diameter absorbing phantoms placed within a large, scattering medium that mimics gel. Excellent linearity between the optical CT scanner and the independent measurement was observed for solutions with between 90% and 2% transmission. These results indicate that the scanner should achieve highly accurate dosimetry of large volume dosimeters in a reasonable timeframe for clinical application to radiotherapy dose verification procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".