Impact of energy variation on Cone Ratio, PDD10, TMR20/10 and IMRT doses for flattening filter free (FFF) beam of TomoTherapy Hi-Art(TM)machines.
Bibliographic record
Abstract
PURPOSE: The beam energy (PDD10: Percent depth dose) of a Tomotherapy Hi-ArtTM machine was varied in a controlled experiment from -1.64 to +1.66%, while keeping the output at 100% and the effect of this on IMRT output, MU chamber ratio (MUR), cone ratio (CR) and Tissue Maximum Ratio (TMR20/10) was studied METHODS: In this study, Injector Current Voltage (VIC) and Pulse Forming Network Voltage (VPFN) were changed in steps such that the PDD10 was varied from golden beam value incrementally between -1.64 to +1.66%. The effect of this on other energy indicators was studied to verify the sensitivity of TMR20/10, MUR, and detector data-based-CR. To quantify the effect of energy variation on Intensity Modulated Radiation Therapy (IMRT) dose, multiple ion-chamber based dose measurements were recorded by irradiating a cylindrical phantom with standard IMRT plans. Dose variation across each commissioned Field width (FW) was tabulated against energy variation. RESULTS: Good agreement between PDD10 and TMR20/10, MUR, CR was observed. CR was more sensitive to energy change than PDD10. More variation was observed across standard IMRT plan with increasing energy. CONCLUSION: CR is more sensitive to energy changes compared to PDD10, and CR with MUR can definitely be used as surrogates for checks on a daily/weekly basis. Variation in output across the 6 standard IMRT plans can vary up to 2.8% for a 1.6% increase in energy. Hence, it is of utmost importance to manage the PDD10 tightly around +0.5% in order to regulate standard IMRT QA agreement to within 1% and patient IMRT QA within ±3%.
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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 | 0.002 |
| 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".