On the use of the MLC dosimetric leaf gap as a quality control tool for accurate dynamic IMRT delivery
Bibliographic record
Abstract
PURPOSE: MLC leaf gap consistency is critical for the accurate delivery of dynamic IMRT plans. It is estimated that a systematic MLC leaf gap change of 0.6 mm will result in a 2% change to the equivalent uniform dose to a clinical target volume for a typical head and neck sliding window (SW) IMRT plan. The aim of this work is to use the measured dosimetric leaf gap (DLG) to verify the dosimetric reproducibility of dynamically delivered SW IMRT plans. This study focuses on Varian linacs equipped with the 120 Millennium MLC and the Eclipse treatment planning system (TPS), but can be extended to other linac/MLC/TPS combination. METHODS: An ionization chamber, a diode array, and an electronic portal imaging device (EPID) were used to assess the DLG in zero (central axis), one, and two dimensions, respectively. The DLG for zero and two dimensions was derived from measurements of SW fields of decreasing width (2, 1.5, 1, and 0.5 cm). The DLG in one dimension was measured directly from a single SW sweeping across a linear diode array. This one-dimensional DLG measurement was based on the full width at half maximum (FWHM) of the dose rate versus time spectrum. RESULTS: The DLG derived from ion chamber measurements at central axis agrees to within 0.1 mm, with the DLG measured directly from the FWHM of dose rate versus time spectrum. The measured DLG depends on the control points used for the MLC SW fields. When two control points were used, the DLG measured at central axis showed an increase of 0.6 mm with respect to the same measurements performed using three or more control points. The two-dimensional distribution of DLG obtained using the EPID identified leaf gap errors as small as +/- 0.2 mm in isolated areas away from central axis. CONCLUSIONS: Comprehensive measurements of the DLG in 0D, 1D, and 2D provide an accurate assessment of DLG value required during TPS commissioning. These DLG measurements can also be used as a quality control tool to quantify changes of the MLC calibration and leaf gap consistency, which is critical for the accurate delivery of dynamically delivered SW IMRT plans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".