Dynamic imaging of the fetal heart using metric optimized gating
Bibliographic record
Abstract
PURPOSE: Advances in fetal cardiovascular magnetic resonance imaging have been limited by the absence of a reliable cardiac gating signal. The purpose of this work was to develop and validate metric-optimized gating (MOG) for cine imaging of the fetal heart. THEORY AND METHODS: Cine MR and electrocardiogram data were acquired in healthy adult volunteers for validation of the MOG method. Comparison of MOG and electrocardiogram reconstructions was performed based on the image quality for each method, and the difference between MOG and electrocardiogram trigger times. Fetal images were also acquired, their quality evaluated by experienced radiologists, and the theoretical error in the MOG trigger times were calculated. RESULTS: Excellent agreement between electrocardiogram and MOG reconstructions was observed. The experimental errors in adult MOG trigger times for all five volunteers were ± (7, 25, 17, 8, and 13) ms. Fetal images captured normal and diseased cardiac dynamics. CONCLUSION: MOG for cine imaging of the fetal myocardium was developed and validated in adults. Using MOG, the first gated MR images of the human fetal myocardium were obtained. Small moving structures were visualized during radial contraction, thus capturing normal fetal cardiac wall motion and permitting assessment of cardiac function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".