Does Resampled Image Data Offer Quantitative Image Quality Benefit for Pediatric CT?
Why this work is in the frame
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Bibliographic record
Abstract
Acquiring CT images with thin slices can improve resolution and detectability, but cause an increase in the image noise. To compensate for the additional image noise, the kVp or mA can be increased, which carries a dose penalty to the patient. We investigate the image quality achieved in MPR images reformatted from different slice thicknesses 0.625 mm and 5 mm, to determine if a thicker slice could be resampled to smaller thickness with minimal loss of image information. Catphan?600 phantom was imaged using selected kVp/mA settings (80 kVp/250 mA, 100 kVp/ 150 mA and 120 kVp/200 mA) to generate slices with thicknesses of 0.625 mm and 5 mm using a GE Discovery HD750 64-slice CT scanner to investigate the impact of the acquisition slice thickness on the overall image quality in MPRs. Measurements of image noise, uniformity, contrast-to-noise ratio (CNR), low contrast detectability and limiting spatial resolution were performed on axial and coronal multiplanar reformatted images (MPRs). Increased noise, reduced contrast-to-noise ratio, and improved limiting spatial resolution and low contrast detection were observed in 2 mm coronal MPRs generated with 0.625 mm thin slices when compared to the MPRs from 5 mm thick slices. If the 2 mm coronal MPRs acquired with 5 mm slices are resampled to 0.6 mm slice thickness, the reductions in limiting resolution and low contrast detection are compensated, although with reduced uniformity and increased image noise. Thick slice image acquisitions yield better CNR and less noise in the images, whereas thin slices exhibited improved spatial resolution and low contrast detectability. Retrospectively resampling into thinner slices before obtaining the coronal MPRs provided a balance between image smoothness and identifying fine image detail. Which approach provides the optimal image quality may also depend on the imaging task, size and composition of the features of interest, and radiologist preference.
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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 | 0.003 | 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 | 0.001 |
| 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