Technical Note: Evaluation of kV CBCT enhancement using a liver‐specific contrast agent for stereotactic body radiation therapy image guidance
Bibliographic record
Abstract
PURPOSE: USA, Bayer Healthcare, Leverkusen, Germany), a contrast agent that is taken up selectively by liver cells and is retained for up to an hour. Image quality from CBCT was benchmarked against helical fan-beam computed tomography for two phantom geometries. METHODS AND MATERIALS: Concentrations were diluted to 0.0125-0.1 mmol per kilogram of body weight (mmol/kg) corresponding to expected physiological concentrations in the liver. Kilovoltage CBCT imaging parameters of x-ray tube potential, current, and filtration were investigated using clinically available options on a TrueBeam STx linear accelerator CBCT platform. Two phantoms were created, a cylindrical idealized imaging geometry and an ellipsoidal more realistic abdominal geometry. All parameters were optimized according to the contrast-to-noise ratio (CNR) image quality metric, as a function of concentration, following the Rose criterion for CNR. RESULTS: Acceptable CNR was defined as greater than or equal to three, in accordance with the Rose criterion for CNR. These were found in a range of expected liver concentrations of 0.025-0.1 mmol/kg for a tube potential of 100 kVp, half-fan bowtie filtration and tube currents giving exposures between 2025 and 5085 mAs. Linear correlations were found for all CNR as a function of concentration, in agreement with the literature. CONCLUSION: Based on this phantom study, with appropriate selection of imaging protocol, Gadoxetate Disodium may provide useful liver CBCT enhancement at physiologically achievable liver concentrations.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".