Poster - Thurs Eve-26: Influence of MLC leaf edge and tongue and groove effect on IMRT dose distributions
Bibliographic record
Abstract
An important consideration when using multileaf collimators (MLCs) for IMRT delivery is the correct account of leaf edge effects. To study these effects, two maps were constructed from a clinical beam's step and shoot delivery sequence: a time-weighted leaf edge position (LEP) map where the pixel intensity was proportional to the length of time that a leaf edge defined the edge of any segment within the field, and a (TG) map where pixel intensity was proportional to the length of time that adjacent segments matched along a leaf edge. We investigated the correlation between LEP or TG maps with dose error maps (obtained by subtracting calculated from either measured (CM) or from re-calculated (CC) data). Re-calculated data were obtained by modifying selected MLC photon modeling parameters from their commissioned values. We calculated the correlation coefficient between corresponding regions of CM and CC maps with TG and LEP maps. A NAT analysis of the CM maps indicated that the NAT index was minimized for tongue and groove width at the commissioned value of 0.1cm. A higher correlation coefficient was seen between CM and LEP maps (0.62±0.11) than between CM or CC and TG maps across all MLC modeling parameters used. The low correlation between both LEP or TG maps and CC maps suggests that the higher correlation observed between both LEP or TG maps and CM maps cannot be attributed to the choice of MLC modeling parameters alone. Further work is needed to pinpoint the cause of this correlation.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".