<scp>MLC</scp>‐based penumbra softener of <scp>EDW</scp> borders to reduce junction inhomogeneities
Bibliographic record
Abstract
Junctions of fields are known to be susceptible to developing cold or hot spots in the presence of even small geometrical misalignments. Reduction of these dose inhomogeneities can be accomplished through decreasing the dose gradients in the penumbra, but currently it cannot be done for enhanced dynamic wedges (EDW). An MLC-based penumbra softener was developed in the developer mode of TrueBeam linacs to reduce dose gradients across the side border of EDWs. The movement of each leaf was individually synchronized with the movement of the dynamic Y jaw to soften the penumbra in the same manner along the entire field border, in spite of the presence of the dose gradient of the EDW. Junction homogeneity upon field misalignment for side-matched EDWs was examined with the MV imager. The fluence inhomogeneities were reduced from about 30% per mm of shift of the field borders for the conventional EDW to about 2% per mm for the softened-penumbra plan. The junction in a four-field monoisocentric breast plan delivered to the Rando phantom was assessed with film. The dose inhomogeneities across the junction in the superior-inferior direction were reduced from about 20% to 25% per mm for the conventional fields to about 5% per mm. The dose near the softened junction of the breast plan with no shifts did not deviate from the conventional plan by more than about 4%. The newly-developed softened-penumbra junction of EDW (and/or open) fields was shown to reduce sensitivity to misalignments without increasing complexity of the planning or delivery. This methodology needs to be adopted by the manufacturers for clinical use.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".