Feasibility of phase-contrast cine magnetic resonance imaging for measuring blood flow in the sheep fetus
Bibliographic record
Abstract
Phase-contrast cine MRI (PC-MRI) is the gold-standard noninvasive technique for measuring vessel blood flow and has previously been applied in the human fetal circulation. We aimed to assess the feasibility of using PC-MRI to define the distribution of the fetal circulation in sheep. Fetuses were catheterized at 119–120 days of gestation (term, 150 days) and underwent MRI at ∼123 days of gestation under isoflurane anesthesia, ventilated at a [Formula: see text] of 1.0. PC-MRI was performed using a fetal arterial blood pressure catheter signal for cardiac triggering. Blood flows were measured in the major fetal vessels, including the main pulmonary artery, ascending and descending aorta, superior vena cava, ductus arteriosus, left and right pulmonary arteries, umbilical vein, ductus venosus, and common carotid artery and were indexed to estimated fetal weight. The combined ventricular output, pulmonary blood flow, and flow across the foramen ovale were calculated from vessel flows. Intraobserver and interobserver agreement and reproducibility was assessed. Blood flow measurements were successfully obtained in 61 out of 74 vessels (82.4%) interrogated in 9 fetuses. There was good intraobserver [ R = 0.998, P < 0.0001; intraclass correlation (ICC) = 0.997] and interobserver agreement ( R = 0.996, P < 0.0001; ICC = 0.996). Repeated MRI measurements showed good reproducibility ( R = 0.989, P = 0.0002; ICC = 0.990). We conclude that PC-MRI using fetal catheters for gating triggers is feasible in the major vessels of late gestation fetal sheep. This approach may provide a useful new tool for assessing the circulatory characteristics of fetal sheep models of human disease, including fetal growth restriction and congenital heart disease.
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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 | 0.005 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".