Successful Dose Reduction Using Reduced Tube Voltage With Hybrid Iterative Reconstruction in Pediatric Abdominal CT
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to assess radiation dose reduction, image quality, and diagnostic confidence using low tube voltage in combination with hybrid iterative reconstruction in contrast-enhanced pediatric abdominal CT. MATERIALS AND METHODS: CT examinations of 133 patients (median age, 10 years) were performed at sequentially reduced doses. The first group (group 1) was scanned using dimension-based protocols at 120 kV for all patient sizes. The optimized group (group 5) was scanned at 80 kV for less than 18 cm in the lateral dimension and 100 kV in the 19-30 cm lateral dimension. CT examinations reconstructed with filtered back projection (FBP) and four levels of hybrid iterative reconstruction were reviewed by four blinded readers for subjective image quality and diagnostic confidence. Objective noise, volume CT dose index (CTDIvol), and size-specific dose estimate (SSDE) were recorded. Data were analyzed using t tests, one and two-way ANOVA, and the intraclass correlation coefficient. RESULTS: Compared with group 1, the radiation dose was reduced for group 5 by 63% measured by SSDE (4.69 vs 10.00 mGy; p < 0.001). Subjective image noise was increased for FBP images (p < 0.001) but not was statistically significantly different for all levels of hybrid iterative reconstruction; artifacts were reduced and visibility of small structures was improved (both p < 0.001). Diagnostic confidence was improved for solid organ injury and metastatic disease (both p < 0.001) and was not statistically significantly different for appendicitis (p = 0.306). CONCLUSION: Use of hybrid iterative reconstruction with low-tube-voltage protocols enables substantial radiation dose reduction for pediatric abdominal CT with equivalent to improved subjective image quality and diagnostic confidence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".