Sci—Thur PM: YIS — 04: Aperture Superposition Algorithm for Photon Dose Calculations of Finite Size Cobalt‐60 Radiation Source for Tomotherapy Dose Deliveries
Bibliographic record
Abstract
The finite size pencil beam (FSPB) superposition method is commonly used to calculate dose for intensity modulated beams in IMRT. The FSPB model assumes that broad beam dose from a radiation originating from a point source can be calculated by a superposition of dose from pencil beams. For finite size sources, such as a Cobalt‐60 (Co‐60) source of 2 cm diameter, this method is no longer valid. In this paper we propose an aperture superposition (AS) dose calculation method that can be used for dose calculations of intensity modulated beams from finite size radiation sources. The model is applied to fan beams, as encountered in tomotherapy, and results are compared to the FSPB model and the film measurements. The comparisons between the AS model and film measurements show agreement to 1.5% in the high dose regions and 3.7% in the low dose regions. On the other hand, film measurement comparisons to the FSPB model show that the FSPB model underestimates the dose by up to 7% for small field sizes such as 2×2cm2 and 20% for larger field sizes such as 20×2 cm2. In conclusion, the AS model provides a better accuracy than the FSPB model when calculating dose for fan beams from large radiation sources. Research is underway to extend the application of this model to broad IMRT beams obtained from non‐binary multi‐leaf collimators. The implementation of this model to the current treatment planning systems can be useful for treatment planning of Co‐60 based IMRT and tomotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.017 | 0.006 |
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".