Sci‐AM2 Sat ‐ 07: Development of inverse planning and limited angle CT reconstruction for cobalt‐60 tomotherapy
Bibliographic record
Abstract
A significant amount of evidence exists to suggest that Cobalt‐60 can be used to deliver conformal treatments with intensity modulated tomotherapy and on‐line image guidance with megavoltage CT. The purpose of this paper is to describe our recent advances in developing the potential for Cobalt‐60 as a modality for tomotherapy. In particular, we have been advancing forward and inverse planning designed especially for our benchtop delivery configuration, on‐line image reconstruction for Cobalt‐60 CT, and approaches to dose reconstruction. Treatment planning is performed by dose optimization under implicit dose‐volume constraints for a slice using a gradient based inverse algorithm. Although a least‐squares framework is maintained in the explicit objective function for rapid convergence, regional weightings evolve over time to attempt to accommodate dose‐volume constraints. The selection of the appropriate relative region weightings in the objective function is handled internally to improve ease‐of‐use. Simulated plans under a variety of dose‐volume constraints reach reasonable solutions in a matter of minutes. The second area of focus has been in the creation of a CT method that is amenable to the deficiencies of image reconstruction under the inherently limited set of treatment data, while maintaining a high speed of reconstruction necessary for on‐line registration. A one‐step reconstruction method belonging to the class of algebraic reconstruction techniques (ART) is being tested and has shown promise in providing a compromise between the limited‐view qualities of iterative ART and the high reconstruction speed of filtered back‐projection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.004 |
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".