Comparison of high spatial resolution respiratory triggered inversion recovery‐prepared spoiled gradient echo sequence with standard breathhold T1 sequence MRI of the liver using gadoxetic acid
Bibliographic record
Abstract
PURPOSE: To assess if a high resolution respiratory triggered inversion recovery prepared GRE sequence (RT) improved image quality and detection of lesions compared with breathhold GRE T1 weighted MR sequence (BH) in the hepatobiliary uptake phase of MR of the liver using gadoxetic acid (Gd-EOB-DTPA). MATERIALS AND METHODS: Thirty-eight consecutive patients from July 2009 to September 2010 who had undergone Gd-EOB-DTPA enhanced liver exams were retrospectively identified. Qualitative assessment performed on reference lesions and background liver by two independent readers. Quantitative assessment performed by one reader. RESULTS: Liver parenchyma signal-to-noise ratio for BH was 90.3 ± 23.9 (mean ± SD) and RT, 106.1 ± 40.4 (P = 0.119). For BH, 320 lesions were detected compared with 257 for RT. Lesion to liver contrast was significantly better on RT sequences (0.26 ± 0.24; mean ± SD) compared with BH sequence (0.21 ± 0.20; P = 0.044). Fifty-seven reference lesions assessed. Both reviewers rated BH better for lesion margin and hepatic vessel sharpness. BH was rated with less artifact (P < 0.05). Lesion to liver contrast on BH was significantly better for one reviewer. CONCLUSION: BH sequence had better overall image quality than RT in several quantitative and qualitative factors including number of lesions detected and level of artifact.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".