The use of modified single pencil beam dose kernels to improve IMRT dose calculation accuracy
Bibliographic record
Abstract
Intensity modulated radiation therapy (IMRT) is used to deliver highly conformal radiation doses to tumors while sparing nearby sensitive tissues. Discrepancies between calculated and measured dose distributions have been reported for regions of high dose gradients corresponding to complex radiation fluence patterns. For the single pencil beam convolution dose calculation algorithm, the ability to resolve areas of high dose structure is partly related to the shape of the pencil beam dose kernel (similar to how a photon detector's point spread function relates to imaging resolution). Improvements in dose calculation accuracy have been reported when the treatment planning system (TPS) is recommissioned using high-resolution measurement data as input. This study proposes to improve the dose calculation accuracy for IMRT planning by modifying clinical dose kernel shapes already present in the TPS, thus avoiding the need to reacquire higher resolution commissioning data. The in-house optimization program minimizes a cost-function based on a two-dimensional composite dose subtraction/distance-to-agreement (gamma) analysis. The final modified kernel shapes are reintroduced into the treatment planning system and improvements to the dose calcula tion accuracy for complex IMRT dose distributions evaluated. The central kernel value (radius =0 cm) has the largest effect on the dose calculation resolution and is the focus of this study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".