TU‐C‐304A‐09: Radiation Dose Reduction for Pediatric CT
Bibliographic record
Abstract
Purpose: The purpose of this study was to evaluate potential radiation dose reduction for pediatric CT. Materials and Methods: A dose‐reduction simulation tool, which adds synthetic noise to raw projection measurements and reconstructs images at a simulated lower dose (Massoumzadeh, et al. Med. Phys. Vol. 36, pp. 174–189, 2009), was used to simulate low‐dose CT images. Simulated low‐dose CT images are created from full‐dose CT images of normal and pathological slices (lung nodules and abdominal visceral lesions). The amount of added noise was task dependent, with 10 sets of simulated low dose ranged from 1% to 85% of original dose. Sixteen pediatric cases were selected, including eight normal, three patients with pulmonary nodules, two patients with abdominal visceral organ lesions, and three patients with appendicitis without perforation. All studies performed on a 16‐row scanner, with the effects of tube current modulation and bow tie filters included. Following a short training session, 19 volunteer radiologists, from various clinical centers in the world who were attending the International Pediatric Radiology conference in Montreal in 2006, used a 5‐point scale to rate a sequence of simulated, low‐to‐high exposure images for the presence or absence of lesions. They were total of 176 images with viewing sessions limited to 45 minutes. Diagnostic agreement between full‐dose and reduced‐dose images was assessed with a weighted Kappa statistic. Result: For detection of pulmonary nodules, a decrease in the average intra‐observer agreement (kappa= 0.90) was found at 80% dose reduction, while for the detection of abdominal lesions or appendicitis a 50% reduction was observed. Conclusion: There is potential for dose reduction in CT studies, which is task dependent and greater for pulmonary nodule than for abdominal visceral lesions and appendicitis. The noise simulation methodology is a powerful tool to help understand the relationship among dose, noise, and observer agreement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".