Asymmetric fan beams (AFB) for improvement of the craniocaudal dose distribution in helical tomotherapy delivery
Bibliographic record
Abstract
Helical tomotherapy (HT) is a novel radiotherapy technique that utilizes intensity modulated fan beams that deliver highly conformal dose distributions in a helical beam trajectory. The most significant limitation in dose delivery with a constant fan beam thickness (FBT) is the penumbra width of the dose distribution in the craniocaudal direction, which is equivalent to the FBT. We propose to employ a half-blocked fan beam at start and stop location to reduce the penumbra width by half. By opening the jaw slowly during the helical delivery until the desired FBT is achieved it is possible to create a sharper edge in the superior and inferior direction from the target. The technique was studied using a tomotherapy beam model implemented on a commercial treatment planning system (Theraplan Plus V3.0). It was demonstrated that the dose distribution delivered using a 25 mm fan beam can be improved significantly, to reduce the dose to normal structures located superiorly and inferiorly of the target. Dosimetry for this technique is straightforward down to a FBT of 15 mm and implementation should be simple as no changes in couch movement are required compared to a standard HT delivery. We conclude that the use of asymmetric collimated fan beams for the start and stop of the helical tomotherapeutic dose delivery has the potential of significantly improving the dose distribution in helical 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".