SWIMRT: A graphical user interface using the sliding window algorithm to construct a fluence map machine file
Bibliographic record
Abstract
A custom-made computer program, SWIMRT, to construct "multileaf collimator (MLC) machine" file for intensity-modulated radiotherapy (IMRT) fluence maps was developed using MATLAB and the sliding window algorithm. The user can either import a fluence map with a graphical file format created by an external treatment-planning system such as Pinnacle3 or create his or her own fluence map using the matrix editor in the program. Through comprehensive calibrations of the dose and the dimension of the imported fluence field, the user can use associated image-processing tools such as field resizing and edge trimming to modify the imported map. When the processed fluence map is suitable, a "MLC machine" file is generated for our Varian 21 EX linear accelerator with a 120-leaf Millennium MLC. This machine file is transferred to the MLC console of the LINAC to control the continuous motions of the leaves during beam irradiation. An IMRT field is then irradiated with the 2D intensity profiles, and the irradiated profiles are compared to the imported or modified fluence map. This program was verified and tested using film dosimetry to address the following uncertainties: (1) the mechanical limitation due to the leaf width and maximum traveling speed, and (2) the dosimetric limitation due to the leaf leakage/transmission and penumbra effect. Because the fluence map can be edited, resized, and processed according to the requirement of a study, SWIMRT is essential in studying and investigating the IMRT technique using the sliding window algorithm. Using this program, future work on the algorithm may include redistributing the time space between segmental fields to enhance the fluence resolution, and readjusting the timing of each leaf during delivery to avoid small fields. Possible clinical utilities and examples for SWIMRT are given in this paper.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.033 |
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".