Sci-Fri PM: Delivery - 10: Imaging Developments for Broad Beam Co-60 Radiation Therapy
Bibliographic record
Abstract
Accessibility is essential to providing quality health care. Our research work has shown that Cobalt-60 (Co-60) treatment can be modernized to provide intensity modulated radiation therapy (IMRT). The use of Co-60 as a radiation source could provide a solution in parts of the world with limited infrastructure to support linac therapy. Previous work has been focused on tomotherapy; however, we have recently expanded to broad-beam IMRT. This could potentially allow the existing fleet of over one thousand Co-60 units to be upgraded instead of replaced. On-board image guidance would be necessary to ensure tumour localization for precise IMRT treatment. Recent acquisition of an amorphous silicon (a-Si) PortalVision aS500 (Varian Medical Systems, Palo Alto, CA) imaging panel has allowed testing of broad beam imaging modalities using a Co-60 therapy unit (Best Theratronics T780C, Kanata, ON). Imaging with the therapy source could avoid the requirement of an additional lower activity source or kV imaging system. Portal imaging and cone-beam computed tomography (CBCT) are widely used for image guidance with broad beam IMRT. Tomosynthesis imaging can provide depth discrimination information using a limited number of projections. Preliminary results clearly demonstrate that all three modalities are feasible with a therapy Co-60 source and a-Si imaging panel. Co-60 CBCT resolution and image quality are comparable to previous fan-beam scans. Co-60 digital tomosynthesis (DT) is shown to enhance anatomical features at arbitrary depths. Thus DT has the potential to generate more useful information for patient setup verification than portal images while delivering less dose than CBCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
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".