Investigating treatment dose error due to beam attenuation by a carbon fiber tabletop
Bibliographic record
Abstract
Carbon fiber is commonly used in radiation therapy for treatment tabletops and various immobilization and support devices, partially because it is generally perceived to be almost radiotransparent to high-energy photons. To avoid exposure to normal tissue during modern radiation therapy, one must deliver the radiation from all gantry angles; hence, beams often transit the couch proximal to the patient. The effects of the beam attenuation by the support structure of the couch are often neglected in the planning process. In this study, we investigate the attenuation of 6-MV and 18-MV photon beams by a Medtec (Orange City, IA) carbon fiber couch. We have determined that neglecting the attenuation of oblique treatment fields by the carbon fiber couch can result in localized dose reduction from 4% to 16%, depending on energy, field size, and geometry. Further, we investigate the ability of a commercial treatment-planning system (Theraplan Plus v3.8) to account for the attenuation by the treatment couch. Results show that incorporating the carbon fiber couch in the patient model reduces the dose error to less than 2%. The variation in dose reduction as a function of longitudinal couch position was also measured. In the triangular strut region of the couch, the attenuation varied +/- 0.5% following the periodic nature of the support structure. Based on these findings, we propose the routine incorporation of the treatment tabletop into patient treatment planning dose calculations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".