Poster — Thur Eve — 37: Respiratory gating with an Elekta flattening filter free photon beam
Bibliographic record
Abstract
In cases where surgery is not possible for lung cancer treatment, stereotactic body radiation therapy (SBRT) may be an option. One problem when treating this type of cancer is the motion of the lungs caused by the patient's respiration. It is possible to reduce the impact of this movement with the use of respiratory gating. By combining respiratory gating with a flattening filter free (FFF) photon beam linac, the increased treatment time caused by a reduced beam‐on time of respiratory gating methods can be compensated by the inherent increased dose rate of FFF beams. This project's aim is to create hardware and software interfaces allowing free respiration gating on an Elekta Synergy‐S linac specially modified to deliver 6 MV FFF photon beams. First, a printed circuit board was created for reading the signal from a Bellows Belt from Philips (a respiration monitor belt) and transmitting an On/Off signal to the accelerator. A software was also developed to visualize patient respiration. Secondly, a FFF model was created with the Pinnacle treatment planning system from Philips. Gamma (Γ) analysis (2%, 2 mm) was used to evaluate model. For fields going from 5.6 × 5.6 to 12 × 12 cm2, central axis depth dose model fitting shows an average gamma value of 0.2 and 100% of gamma values remain under the Γ = 1 limit. For smaller fields (0.8 × 0.8 and 1.6 × 1.6 cm2), Pinnacle has more trouble trying to fit the measurements, overestimating dose in penumbra and buildup regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".