Amplitude gating for a coached breathing approach in respiratory gated 10 MV flattening filter‐free VMAT delivery
Bibliographic record
Abstract
The purpose of this study was to investigate amplitude gating combined with a coached breathing strategy for 10 MV flattening filter-free (FFF) volumetric-modulated arc therapy (VMAT) on the Varian TrueBeam linac. Ten patient plans for VMAT SABR liver were created using the Eclipse treatment planning system (TPS). The verification plans were then transferred to a CT-scanned Quasar phantom and delivered on a TrueBeam linac using a 10 MV FFF beam and Varian's real-time position management (RPM) system for respiratory gating based on breathing amplitude. Breathing traces were acquired from ten patients using two kinds of breathing patterns: free breathing and an interrupted (~ 5 s pause) end of exhale coached breathing pattern. Ion chamber and Gafchromic film measurements were acquired for a gated delivery while the phantom moved under the described breathing patterns, as well as for a nongated stationary phantom delivery. The gate window was set to obtain a range of residual target motion from 2-5 mm. All gated deliveries on a moving phantom have been shown to be dosimetrically equivalent to the nongated deliveries on a static phantom, with differences in point dose measurements under 1% and average gamma 2%/2 mm agreement above 98.7%. Comparison with the treatment planning system also resulted in good agreement, with differences in point-dose measurements under 2.5% and average gamma 3%/3 mm agreement of 97%. The use of a coached breathing pattern significantly increases the duty cycle, compared with free breathing, and allows for shorter treatment times. Patients' free-breathing patterns contain considerable variability and, although dosimetric results for gated delivery may be acceptable, it is difficult to achieve efficient treatment delivery. A coached breathing pattern combined with a 5 mm amplitude gate, resulted in both high-quality dose distributions and overall shortest gated beam delivery times.
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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 | 0.001 |
| 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) | 0.001 | 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".