Hybrid Iterative Reconstruction Technique for Abdominal CT Protocols in Obese Patients: Assessment of Image Quality, Radiation Dose, and Low-Contrast Detectability in a Phantom
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess the impact of a noise reduction technique on image quality, radiation dose, and low-contrast detectability in abdominal CT for obese patients. MATERIALS AND METHODS: A liver phantom with 12 different tumors was designed, and fat rings were added to mimic intermediately sized and large patients. The intermediate and large phantoms were scanned with our standard abdominal CT protocol (image noise level of 15 HU and filtered back projection [FBP]). The large phantom was scanned with five different noise levels (10, 12.5, 15, 17.5, and 20 HU). All datasets for the large phantom were reconstructed with FBP and the noise reduction technique. The image noise and the contrast-to-noise ratio (CNR) were assessed. Tumor detection was independently performed by three radiologists in a blinded fashion. RESULTS: The application of the noise reduction method to the large phantom decreased the measured image noise (range, -14.5% to -37.0%) and increased the CNR (range, 26.7-70.6%) compared with FBP at the same noise level (p < 0.001). However, noise reduction was unable to improve the sensitivity for tumor detection in the large phantom compared with FBP at the same noise level (p > 0.05). Applying a noise level of 15 HU, the overall sensitivity for tumor detection in the intermediate and large phantoms with FBP measured 75.5% and 87.7% and the radiation doses measured 42.0 and 23.7 mGy, respectively. CONCLUSION: Although noise reduction significantly improved the quantitative image quality in simulated large patients undergoing abdominal CT compared with FBP, no improvement was observed for low-contrast detectability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".