Improving the resolution of dynamic intensity modulated radiation therapy delivery by reducing the multileaf collimator sampling distance
Bibliographic record
Abstract
The conformality of a dose distribution delivered by a multileaf collimator (MLC) for intensity modulated radiation therapy (IMRT) is limited in the direction perpendicular to leaf motion by the finite leaf width. Two methods of improving the resolution of IMRT intensity maps in this direction were investigated. In the first, the desired fluence distribution is considered to be sampled by the MLC, with the sampling distance being the center-to-center distance between the MLC leaves. The sampling distance is reduced below the leaf width by combining separate irradiations with a couch shift between them. This has been applied to static field therapy [Galvin et al., Int. J. Radiat. Oncol., Biol., Phys. 35, 89-94 (1996)], and was proposed for IMRT by Bortfeld et al. [Med. Phys. 27, 2494-2502 (2000)]. In the second method, two MLC component fluences, with leaf width L = 2deltay and offset by deltay, are combined to reproduce desired intensity bins with deltay width. The effect of MLC leaf sampling distance on dose resolution was quantified for both 1.0 and 0.5 cm MLC leaf widths, utilizing a high resolution bar-pattern fluence, an annular shaped fluence, and an intensity step-edge. Improvement in resolution was found for the 1.0 cm leaf width at a sampling distance of 0.5 cm, with only a small benefit for further reduction. For the 0.5 cm leaf width, a sampling distance of 0.25 cm resulted in a dose resolution that was nearly independent of direction.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".