A multi-institutional assessment of low-dose protocols in chest computed tomography: Dose and image quality
Bibliographic record
Abstract
Background Low-dose CT (LDCT) chest protocols have widespread clinical applications for many indications; as a result, there is a need for protocol assessment prior to standardization. Dalhousie University and Oslo Metropolitan University have a formally established cooperative relationship. Purpose The purpose is to assess radiation dose and image quality for LDCT chest protocols in seven different hospital locations in Norway and Canada. Material and methods Retrospective dosimetry data, volumetric CT dose index (CTDI vol ), and dose length product (DLP) from 240 average-sized patients as well as CT protocol parameters were included in the survey. Effective dose (ED) and size-specific dose estimate (SSDE) were calculated for each examination. For a quantitative image quality analysis, noise, CT number, and signal-to-noise ratio (SNR) were determined for three regions in the chest. The contrast-to-noise ratio (CNR) was calculated for lung parenchyma in comparison to the subcutaneous fat. Differences in dose and image quality were evaluated by a single-factor ANOVA test. A two-sample t-test was performed to determine differences in means between individual scanners. Results The ANOVA test revealed significant differences ( p < .05) in dose values for all scanners, including identical scanner models. Statistically significant differences ( p < .05) were determined in mean values of the SNR distributions between the scanners in all three measured regions in the chest, as well as the CNR values. Conclusion The observed variations in dose and image quality measurements, even within the same hospitals and between identical scanner models, indicate a potential for protocol optimization in the involved hospitals in both countries.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".