Cone beam optical computed tomography for gel dosimetry I: scanner characterization
Bibliographic record
Abstract
The ongoing development of easily accessible, fast optical readout tools promises to remove one of the barriers to acceptance of gel dosimetry as a viable tool in cancer clinics. This paper describes the characterization of a number of basic properties of the Vista cone beam CCD-based optical scanner, which can obtain high resolution reconstructed data in less than 20 min total imaging and reconstruction time. The suitability of a filtered back projection cone beam reconstruction algorithm is established for optically absorbing dosimeters using this scanner configuration. The system was then shown to be capable of imaging an optically absorbing media-filled 1 L polyethylene terephthalate (PETE) jar dosimeter to a reconstructed voxel resolution of 0.5 x 0.5 x 0.5 mm(3). At this resolution, more than 60% of the imaged volume in the dosimeter exhibits minimal spatial distortion, a measurement accuracy of 3-4% and the mean to standard deviation signal-to-noise ratio greater than 100 over an optical absorption range of 0.06-0.18 cm(-1). An inter-day scan precision of 1% was demonstrated near the upper end of this range. Absorption measurements show evidence of stray light perturbation causing artifacts in the data, which if better managed would improve the accuracy of optical readout. Cone beam optical attenuation measurements of scattering dosimeters, on the other hand, are nonlinearly affected by angled scatter stray light. Scatter perturbation leads to significant cupping artifacts and other inaccuracies that greatly limit the readout of scattering polymer gel dosimeters with cone beam optical CT.
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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 | 0.001 | 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) | 0.002 | 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".