Scanner and kVp dependence of measured CT numbers in the ACR CT phantom
Bibliographic record
Abstract
Quality control testing of CT scanners in our region includes a measurement of CT numbers in the American College of Radiology (ACR) CT phantom using a standardized protocol. CT number values are clinically relevant in determining the composition of various tissues in the body. Accuracy is important in the characterization of tumors, assessment of coronary calcium, and identification of urinary stone composition. Effective quality control requires that tolerance ranges of CT number values be defined: a measured value outside the range indicates the need for further investigation and possible recalibration of the scanner. This paper presents the results of CT number measurements on 36 scanners (25 GE, 10 Siemens and 1 Toshiba) at each available kVp. Among the five materials (solid water, air, polyethylene, acrylic, bone-equivalent) the measured CT numbers exhibit manufacturer and kVp dependence, which should be taken into account when defining tolerances. With this scan protocol, air and solid water values are significantly higher on GE scanners than on Siemens scanners (p-value < 0.01 at each kVp). The CT numbers of polyethylene and acrylic increase with kVp, while the bone-equivalent CT number decreases. These results are used to define manufacturer- and kVp-specific tolerance ranges for the CT numbers of each material in this phantom, which will be used in our quality control program.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".