Reduced Radiation Dose Helical Chest CT: Effect on Reader Evaluation of Structures and Lung Findings
Bibliographic record
Abstract
PURPOSE: To assess, by using computer simulation, the effect of the use of reduced computed tomographic (CT) tube current on reader evaluation of structures and lung findings on images obtained at clinically indicated chest CT examinations. MATERIALS AND METHODS: The noise level in the raw scan data of 150 clinically indicated conventional tube current (200-320-mA) chest CT examinations was modified to simulate tube current reduction to 100 and to 40 mA. A total of 450 image sets were thus available. Four radiologists blinded to the tube current used assessed the image sets in random order for 14 structures and lung findings and ranked subjective image quality by using a five-point scale (1 = nondiagnostic, 2 = inferior, 3 = adequate, 4 = good, 5 = excellent). After a 3-week interval, the 150 conventional tube current image sets were rescored so that intraobserver agreement could be assessed. The McNemar statistic was used to determine whether there were more scoring disagreements between interpretations of the conventional and those of the reduced tube current scans or between the two interpretations of the conventional tube current scans. RESULTS: When overall agreement for 14 structures and lung findings was pooled over four observers, significantly more disagreements (P <.05) were seen when scores were compared between conventional and reduced tube current scans than when scores were compared between repeated interpretations of the conventional tube current scans. There was a significant decrease (P <.05) in the subjective image quality of reduced tube current scans compared with the subjective image quality of conventional tube current scans. CONCLUSION: These data indicate that reduced tube current does affect reader evaluation of structures and lung findings and reduces a reader's subjective assessment 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".