Comprehensive fluence delivery optimization with multileaf collimation
Bibliographic record
Abstract
Abstract We propose a novel comprehensive model of the dynamic multileaf collimator (MLC) sequencing problem under the sliding window technique. While the majority of research regarding MLC sequencing has lain dormant for years, leaf sequencing is currently done via ‘black-box’ implementations in clinical cancer treatment planning software. As such, it is unclear which leaf motion and fluence transmission parameters are included in these models, given their widely varying analytic and heuristic treatment in the existing literature. We hypothesize that an explicit, comprehensive model may fill an essential role in further research into intensity modulated radiation therapy and volumetric modulated arc therapy. To this end, we consolidate considerations of leaf motion (maximum velocity, finite acceleration), transmission (through-leaf, inter-leaf via tongue and groove) and novel formulations for penumbra across both dimensions of the field. In addition, we formulate our model to utilize these varying transmission effects to optimally sequence leaves with the goal of creating a fluence with pixel size smaller than the narrowest leaf width. To evaluate the proposed model, we have optimized MLC leaf sequencing on 25 prostate, 25 head and neck, 25 pelvis and 35 breast cancer fluence fields. The output sequenced fluences with and without constraints were compared with the corresponding reference fluences, respectively, by the performance of root mean square error and gamma index analysis. The acceptance criteria of 0.5%/0.5 mm and 1%/1 mm were used with a 0%, 5% and 10% low intensity threshold, respectively. Under the consideration of aforementioned constraints, the model can sequence the reference fluence successfully with the percentage of gamma passing rate ranging from 82.23 ± 3.89 to 99.78 ± 0.16 at 0.5%/0.5 mm and from 88.24 ± 1.89 to 99.98 ± 0.03 at 1%/1 mm across all low intensity thresholds and four treatment sites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".