Comparative evaluation of image quality from three CT simulation scanners
Bibliographic record
Abstract
Today, radiation therapy (RT) is moving toward increased radiation dose to the tumor as a result of 3D conformal RT (3DCRT) and intensity-modulated RT (IMRT), which have been made possible by advances in volumetric-based image planning with digital imaging systems such as computed tomography (CT). Treatment planning for such RT requires superior CT image quality. Our goal in this study was to evaluate and to compare the image quality of three unique CT simulation scanners available at our center for both single- and multiple-slice helical scanners. These scanners included a conventional 70-cm bore single-slice scanner (Philips Medical Systems), a large 85-cm bore single-slice scanner (Philips Medical Systems), and a 70-cm bore multislice scanner (GE Medical Systems). Image quality was evaluated in terms of image noise, low-contrast detectability (LCD), limiting spatial resolution (modulation transfer function), and slice thickness accuracy in accordance with guidelines set out by the AAPM. A commercially available Catphan phantom was used to characterize image quality for both axial and helical modes of scanning. We found that image quality was generally comparable for all scanners. Limiting spatial resolution and slice thickness accuracy were comparable for all three scanners for both scanning modes. The multislice unit was superior in terms of noise content, resulting in improved visualization of small, low-contrast objects, which is of significant clinical importance, particularly for soft tissue delineation. In addition, the multislice unit optimizes volume coverage speed and longitudinal resolution without compromising image quality, a significant advantage for the radiation oncology environment.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".