kVCT, MVCT, and hybrid CT image studies—Treatment planning and dose delivery equivalence on helical tomotherapy
Bibliographic record
Abstract
PURPOSE: To determine the equivalence of radiation therapy treatment planning and delivery for various imaging options on helical tomotherapy. METHODS: Seven treatment plans using identical anatomy and planning parameters were created based on the following CT studies: Standard kilovoltage CT (kVCT); 2, 4, and 6 mm spacing megavoltage CT (MVCT); and 2, 4, and 6 mm hybrid MVCT/kVCT studies. In addition, two kVCT based plans were created to explore the effect of the choice of dose calculation grid for optimization. Calculated and measured dose distributions were compared via volumetric and dosimetric analysis at the planning stage, point dose measurements with ion chamber, along with EDR2 film data for gamma function analysis for distance to agreement of 3 mm and dose differences of 3%, 5%, and 7% using both the commercially available "cheese" phantom and the new QUASAR Verification (QVer) phantom. RESULTS: Plans created for each imaging option showed residual error increasing as image slice spacing increased and critical structure size decreased. With the exception of the low dose area in hybrid studies, point dose measurements were within the calculated/measured dose acceptance criteria of 5% on both the QVer and cheese phantoms. Gamma analysis for the original kVCT plan delivery showed an average of 98.5% +/- 0.5% and 98.8% +/- 0.3% of dose pixels passing kVCT study treatment and delivery quality assurance procedures, respectively. The QVer phantom allows for delivery quality assurance with simultaneous use of two films and more convenient gamma function assessment but shows some measurement discrepancy up to 10% compared to the cheese phantom. CONCLUSIONS: The kVCT, MVCT, and kVCT/MVCT hybrid studies showed considerable agreement at both planning and delivery stages. The choice of calculation grid is more important when dealing with smaller anatomical structures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".