SU‐E‐I‐22: Non‐Clinical Applications for Cobalt‐60 Cone Beam CT Imaging
Bibliographic record
Abstract
Purpose: To investigate possible applications of a Cobalt‐60 (Co 60) cone‐beam megavoltage computed tomography (CoCBCT) imaging system, developed for patient positioning on Co‐60 teletherapy machines, in non‐clinical fields such as archaeology and non‐destructive testing. Methods: CoCBCT scans were performed on a series of dense objects requiring nondestructive testing using a bench‐top imaging system consisting of a Theratron 780C Co 60 unit (Best Theratronics, Kanata, ON) and a Varian aS500 amorphous silicon portal imager (Varian, Palo Alto, CA). Images were obtained on a series of heavily concreted artifacts recovered from a 17th century shipwreck at LˈAnse aux Bouleaux in Québec. and on cylindrical test cylinders of hematite‐rich shielding concrete obtained during the construction of new linac bunkers in our cancer centre expansion. The CoCBCT images were generated using the FDK back‐projection algorithm from transmission portal images that were obtained by rotating the objects by 1.5° degree increments through a full rotation. Results: In the case of the shipwreck artifacts, CoCBCT allowed dense metal features such as lead shot to be identified within the concretion layers, and offered a clear view of the shape of voids and other features that would be difficult or impossible to see without damaging the objects. These images will guide subsequent manipulation of the objects during archaeological assessment. CoCBCT images of the concrete cylinders reveal details in the concrete structure, such as voids or aggregate segregation, that would affect the shielding performance of the concrete. Consistent, uniform distribution of the aggregate in most of the samples implied that the mix was stable and not likely to segregate in the shielding walls. Conclusions: Co 60 CBCT is also suitable for non‐destructive imaging of highly attenuating non‐clinical objects, including items with large metal features which would limit accurate imaging using kV CT or broad energy spectrum MV CT. Funding from the Ontario government has been secured through the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO), which matches funding from an industrial partner, Best Theratronics (Kanata, ON).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".