High resolution multi-arterial phase MRI improves lesion contrast in chronic liver disease
Bibliographic record
Abstract
PURPOSE: To determine the reliability of arterial phase capture and evaluate hypervascular lesion contrast kinetics with a combined view-sharing and parallel imaging dynamic contrast-enhanced acquisition, DIfferential Sub-sampling with Cartesian Ordering (DISCO), in patients with known chronic liver disease. METHODS: A retrospective review of 3T MR images from 26 patients with known chronic liver disease referred for hepatocellular carcinoma surveillance or post-treatment follow up was performed. After administration of a gadolinium-based contrast agent, a multiphasic acquisition was obtained in a 28 s breath-hold, from which seven sequential post-contrast image volumes were reconstructed. RESULTS: The late arterial phase was successfully captured in all cases (26/26, 95% CI 87-100%). Images obtained 26 s post-injection had the highest frequency of late arterial phase capture (20/26) and lesion detection (23/26) of any individual post-contrast time; however, the multiphasic data resulted in a significantly higher frequency of late arterial phase capture (26/26, p=0.03) and a higher relative contrast (5.37+/-0.97 versus 7.10+/-0.98, p < 0.01). CONCLUSION: Multiphasic acquisition with combined view-sharing and parallel imaging reliably captures the late arterial phase and provides sufficient temporal resolution to characterize hepatic lesion contrast kinetics in patients with chronic liver disease while maintaining high spatial resolution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".