Optimizing cine MRI for uterine peristalsis: A comparison of three different single shot fast spin echo techniques
Bibliographic record
Abstract
PURPOSE: To determine the optimal single shot fast spin echo (SSFSE) technique by varying interval between image acquisitions for cine MRI of uterine peristalsis. MATERIALS AND METHODS: MRI was performed in 13 premenopausal women (4 normal and 9 with benign pelvic pathology) in various phases of their menstrual cycle. Midsagittal uterus was scanned using a multiphasic SSFSE technique at 2-, 3-, and 4-s intervals over 2 min. Three readers independently and randomly evaluated for peristaltic frequency/2 min, longitudinal direction and intensity of peristalsis in three imaging parameters. Contrast-to-noise ratios (CNRs) were also obtained. RESULTS: Peristaltic frequency for the 2, 3, and 4 s was 2.2 ± 2.3, 3.3 ± 1.5, and 3.6 ± 1.3 waves/2 min, respectively. It increased by 1.5 (95% confidence interval [CI]: 0.31-2.64) waves/2 min with 4 s compared with 2 s. Direction was detected for the 2, 3, and 4 s in 5/13(38%), 9/13(69%) and 12/13(92%) women. Compared with 2 s, intensity of peristalsis in endometrial movement (P = 0.04), signal change of the JZ (P = 0.03), and spread into outer myometrium (P = 0.02), CNRendometrium-JZ by 57% (P < 0.001), and CNRouter myometrium-JZ by 45% (P < 0.01) increased with 4 s. CONCLUSION: Cine MRI with SSFSE sequence for uterine peristaltism is best performed using a 4-s scan interval.
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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 | 0.006 |
| 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) | 0.000 | 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".