SU-GG-T-34: Effects of Target Replacement on Helical MVCT Images for Use in Adaptive Radiotherapy
Bibliographic record
Abstract
Purpose: To investigate the effects of target replacement on helical mega-voltage CT (MVCT) images for use in adaptive radiotherapy. Method and Materials: CT number to density tables were measured for three helical tomotherapy systems at two separate institutions. For each machine, MVCT images and measurements were acquired before and after their respective beam targets were replaced. Phantoms containing removable plugs having known physical densities between 0.6–1.8 g/cm3 were imaged on the helical tomotherapy imaging systems. Physical densities were collected from phantom specifications, and CT numbers were recorded from regions of interests consistently drawn within each phantom plug. Phantom and clinical MVCT images before and after the target replacements were compared. Results: For all three machines, target replacements affected CT number to density tables. In the density range of water, CT numbers before and after target replacement differed by 45, 105, and 56 HU for Machines A, B, and C, respectively. The post-target replacement CT number to density tables for Machines A and B were extremely similar to each other. Images acquired after the target replacement showed qualitative improvements in image quality. Conclusion: The helical tomotherapy imaging system is affected by major hardware changes, which is evident from measured changes in CT number to density tables before and after a target replacement. If MVCT images are used for treatment planning, these images should be monitored with at minimum an equivalent quality assurance program as for conventional CT simulators. In order to reduce dosimetric uncertainties when using these images for treatment planning or adaptive radiotherapy, the integrity of the CT number to density table should be monitored more rigorously in the presence of machine repairs or instabilities.
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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 | 0.005 |
| 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".