Accuracy of Iodine and Calcium Concentrations in Dual Energy Computed Tomography (DECT)
Bibliographic record
Abstract
Objective: The purpose of this study was to assess accuracies of iodine and calcium concentrations in dual energy computed tomography (DECT). Method: This study was performed using an in-house phantom made from polyester resin. The in-house phantom had a diameter of 16 cm and had 10 holes filled with iodine (with concentrations of 5, 7.5, 10, and 15 mg/ml), calcium (with concentrations 200, 300, 500, and 600 mg/ml), water, and air. The in-house phantom was scanned by an Ultrafast kV Switching DECT (GE Revolution) with a tube voltage of 80/140 kV, rotation time of 0.5 s, and tube current variations of 200, 250, 300, 335, and 370 mA. Images was reconstructed to a material density image (MDI). The iodine and calcium concentrations were measured using GSI Viewer software and compared with set iodine and calcium concentrations. Results: It was found that absolute percentage error (APE) of concentrations on iodine was <12% and on calcium was <25% for all concentration variations. Hence, the APE of iodine is smaller than that of calcium. It was found that increasing the tube current does not necessarily improve the accuracy of iodine and calcium concentration measurements. Conclusion: Measurements of iodine and calcium concentrations have been carried out on DECT using an in-house phantom. Overall, iodine and calcium quantification in DECT using the in-house phantom is accurate.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".