Iterative Reconstruction for Head CT: Effects on Radiation Dose and Image Quality
Bibliographic record
Abstract
BACKGROUND: Iterative reconstruction has been reported to reduce radiation dose in CT, while preserving and even improving image quality. The purpose of this study was to evaluate the effects of sinogram-affirmed iterative reconstruction (SAFIRE) on radiation dose reduction and image quality for noncontrast adult head CT and to compare SAFIRE with conventional filtered back-projection (FBP) reconstruction. METHODS: Institutional review board approval was obtained for this retrospective analysis of head CT scans reconstructed with SAFIRE and/or FBP for 107 patients. Radiation dose parameters were recorded from scanner-generated CT dose reports. Signal-to-noise and contrast-to-noise ratios (SNR, CNR) were calculated from gray and white matter (GM, WM) attenuation measurements. Image noise, artifacts, GM-WM differentiation, small structure visibility, and sharpness were graded by two readers. Statistical analysis included the independent-samples t test for quantitative data, the related samples Wilcoxon signed-rank test for qualitative data, the coefficient of repeatability for intraobserver variation, and κ statistics for interobserver agreement. RESULTS: Mean effective dose was significantly reduced with SAFIRE from 2.0 to 1.7 mSv (p<0.0001). SAFIRE also significantly improved GM SNR, WM SNR, and GM-WM CNR (p<0.0001). Significant reductions in image noise and posterior fossa artifact as well as improvements in GM-WM differentiation, small structure visibility, and sharpness were noted with SAFIRE (P<0.005). CONCLUSIONS: SAFIRE for noncontrast adult head CT reduces patient radiation dose by 15% for the settings employed at our institution, while significantly improving multiple quantitative and qualitative measures of image quality.
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".