Radiation dose in abdominal computed tomography: the role of patient size and the selection of tube current.
Bibliographic record
Abstract
OBJECTIVE: To develop an algorithm for selecting tube current for computed tomography (CT), based on patient weight, that produces abdominal CT images of consistent image quality. METHODS: We recorded body weight and radiation exposure parameters for 37 patients undergoing abdominal CT. Two radiologists blind to the CT technique independently graded 11 measures of image quality, using a 5-point (5 = excellent, 4 = good, 3 = acceptable, 2 = poor, and 1 = unacceptable) scale. These scores were averaged to generate an overall image quality score. Using linear regression, we found a target image noise level that corresponded to an overall image quality score of 4.5. We measured CT image noise in 9 uniformly attenuating regions of interest in the liver and abdominal aorta. We used linear regression to assess the relation between tube current and image noise. A prediction equation was developed to set the tube current in different-sized patients to produce images at the target noise level. Finally, we calculated the dose savings that would have resulted with this tube-current selection technique. RESULTS: Image noise was correlated with patient weight (r2 = 0.81). At an overall image quality score of 4.5, the noise was 16 HU. Using this target noise value, we determined the required tube current for each patient weight and found that the use of this technique would have reduced radiation exposure for all patients weighing less than 70 kg. The dose reduction for the smallest patient (35.4 kg) was 72%. CONCLUSION: To produce CT scans of similar quality, a simple prediction equation can be developed for any scanner to optimize radiation dose for patients of all body weights.
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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 | 0.000 | 0.000 |
| 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".