Manually respiratory-triggered single-shot fast spin-echo: a non-breath-hold T2-weighted method for liver lesion detection.
Bibliographic record
Abstract
OBJECTIVES: To determine whether manual respiratory triggering of a T2-weighted single-shot fast spin-echo sequence (M-SSFSE) improves detection and characterization of liver lesions compared with conventional breath-held single-shot fast spin-echo (C-SSFSE) technique. METHODS: M-SSFSE is performed by manually triggering a series of single-slice acquisitions through the liver at end of expiration. There were 171 hepatic lesions in 49 patients. Images were randomized and reviewed by 3 radiologists. Lesions were characterized as hemangiomas, cystic or solid. Dynamic gadolinium-enhanced sequences were used as the reference standard. Contrast-to-noise ratios (CNR) were calculated for all lesions that measured 1 cm or more. RESULTS: M-SSFSE was more sensitive than C-SSFSE in the detection of liver lesions (48%-58% v. 37%-46%, p < 0.001). There were no significant differences in the specificity of lesion detection between the 2 sequences. Artifacts were significantly less severe for M-SSFSE compared with C-SSFSE (p = 0.001). The CNR was significantly higher for all liver lesions on M-SSFSE compared with C-SSFSE (p < 0.001). CONCLUSION: M-SSFSE significantly improved hepatic lesion detection and, in particular, improved characterization of solid liver lesions and hemangiomas compared with C-SSFSE imaging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".