Evaluation of hepatic contrast enhancement with a hepatocyte-specific magnetic resonance imaging contrast agent (gadoxetic acid) in healthy dogs
Bibliographic record
Abstract
OBJECTIVE: To determine, by means of MRI, the time to maximal contrast enhancement in T1-weighted images following IV administration of gadoxetic acid in healthy dogs and assess the impact of gadoxetic acid on the signal intensity of T2-weighted images. ANIMALS: 7 healthy dogs. PROCEDURES: No hepatic abnormalities were detected during ultrasonographic examination. Each dog was anesthetized and positioned in dorsal recumbency for MRI. Transverse T1- and T2-weighted images of the liver were acquired prior to and following (at 5-minute intervals) IV injection of 0.1 mL of gadoxetic acid/kg. Signal intensity of the liver parenchyma was measured in 3 regions of interest in the T1- and T2-weighted images before and at various times point after contrast agent administration. Time versus signal-to-noise ratio curves were plotted to determine time to maximal contrast enhancement and contrast agent-related changes in signal intensity in T2-weighted images. RESULTS: Analysis of T1-weighted images revealed that mean ± SD time to maximal enhancement after gadoxetic acid injection was 10.5 ± 3.99 minutes. Signal intensity of T2-weighted images was not significantly affected by gadoxetic acid administration. No injection-related adverse effects were observed in any dog. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that gadoxetic acid can be used for hepatic MRI in healthy dogs and the resultant hepatic enhancement patterns are similar to those described for humans. Maximal contrast enhancement occurred between 10 and 15 minutes after contrast agent injection; thus, T2-weighted images may be obtained in the interval between injection and maximal enhancement for a more time-efficient clinical protocol.
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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 | 0.000 | 0.001 |
| 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 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".