Poster — Wed Eve—37: Phantom Study Comparing Image Quality of Slow‐CT and Average CT Dataset from 4DCT Radiotherapy Planning Acquisitions
Bibliographic record
Abstract
The purpose of this work was to compare slow‐CT ( ) and average CT ( ) datasets with free‐breathing helical CT ( ) for contouring of organs at risk (OAR) and radiation treatment planning in patients receiving stereotactic body radiation therapy in lung. A quantitative examination of image quality parameters obtained from 4DCT‐derived datasets was performed using a mobile image quality phantom. The rigid phantom was translated in the superior‐inferior and anterior‐posterior directions through the CT scanning plane. Measurements of noise, low and high‐contrast resolution, spatial linearity, sensitometry, and observation of image artifacts resulting from phantom motion were recorded. In addition to these measurements, we computed the mean CT image ( ) by increasing the number of breathing phases in the average CT calculation, and compared those image quality parameters with , and . While the image quality parameters were statistically the same in the phantom study, datasets had fewer image artifacts than the datasets. The image quality of the datasets improved greatly when the number of datasets used to generate the CTN was larger than the maximum permissible bins on the 4DCT reconstruction software (10 phases). or datasets may be used in place of for OAR contouring and potentially dose calculations, without significant compromise in image quality. This work demonstrates that when 4DCT is available, it may not be necessary to acquire a separate scan for OAR contouring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".