Phantom study of the impact of adaptive statistical iterative reconstruction (ASiR<sup>TM</sup>) on image quality for paediatric computed tomography
Why this work is in the frame
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Bibliographic record
Abstract
Quantitative analysis of image quality will be helpful for designing ASiRTM-enhanced paediatric CT protocols, balancing image quality and radiation dose. Catphan600 phantom studies were performed on a GE Discovery HD750 64-slice CT scanner. Images were reconstructed with 0% - 100% ASiRTM (tube current 150 mA, variable kVp 80 - 140) in order to determine the optimal ASiRTM-Filtered Back Projection (FBP) blend. Images reconstructed with a 50% ASiRTM-50% FBP blend were compared to FBP images (0% ASiRTM) over a wide range of kVp (80 - 140) and mA (10 - 400) values. Measurements of image noise, CT number accuracy and uniformity, spatial and contrast resolution, and low contrast detectability were performed on axial and reformatted coronal images. Improvements in CNR, low contrast detectability and radial uniformity were observed in ASiRTM images compared to FBP images. 50% ASiRTM was associated with a 26% - 30% reduction in image noise. Changes in noise texture were observed at higher % ASiRTM blends with impact on visualisation of low and high contrast objects. A small decrease in limiting spatial resolution was detected with addition of ASiRTM, more appreciable at very low tube currents. The preferred blend for paediatric body protocols in our study, as determined by the image quality parameters investigated, was 50% ASiRTM when used with tube currents greater than 50 mA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it