Technical Note: Volumetric computed tomography for radiotherapy simulation and treatment planning
Bibliographic record
Abstract
PURPOSE: For lung and liver tumors requiring radiotherapy, motion artifacts are common in 4D-CT images due to the small axial field-of-view (aFOV) of conventional CT scanners. This may negatively impact contouring and dose calculation accuracy and could lead to a geographic miss during treatment. Recent advancements in volumetric CT (vCT) enable an aFOV up to 160 mm in a single rotation, which may reduce motion artifacts. However, the impact of large aFOV on CT number required for dose calculation needs to be evaluated before clinical implementation. The objective of this study was to determine the utility of a 256-slice vCT scanner for 4D-CT simulation by evaluating image quality and generating relative electron density (RED) curves. METHODS: Images were acquired on a 256-slice GE Revolution CT scanner with 40 mm, 80 mm, 120 mm, 140 mm, and 160 mm aFOV. Image quality was assessed by evaluating CT number linearity, uniformity, noise, and low-contrast resolution. The relationship between each quality metric and aFOV was assessed. RESULTS: CT number linearity, uniformity, noise, and low-contrast resolution were within the expected range for each image set, except CT number in Teflon and Delrin, which were underestimated. Spearman correlation coefficient (ρ) showed that the CT number for Teflon (ρ = 1.0, p = 0.02), Delrin (ρ = 1.0, p = 0.02), and air (ρ = 1.0, p = 0.02) was significantly related to aFOV, while all other measurements were not. The measured deviations from expected values were not clinically significant. CONCLUSION: These results suggest that vCT can be used for CT simulation for radiation treatment planning.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.033 |
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".