Metric optimized gating for fetal cardiac MRI
Bibliographic record
Abstract
Phase-contrast magnetic resonance imaging can be used to complement echocardiography for the evaluation of the fetal heart. Cardiac imaging typically requires gating with peripheral hardware; however, a gating signal is not readily available in utero. No successful application of existing technologies to human fetal phase-contrast magnetic resonance imaging has been reported to date in the literature. The purpose of this work is to develop a technique for phase-contrast magnetic resonance imaging of the fetal heart that does not require measurement of a gating signal. Metric optimized gating involves acquiring data without gating and retrospectively determining the proper reconstruction by optimizing an image metric. The effects of incorrect gating on phase contrast images were investigated, and the time-entropy of the series of images was found to provide a good measure of the level of corruption. The technique was validated with a pulsatile flow phantom, experiments with adult volunteers, and in vivo application in the fetal population. Images and flow curves from these measurements are presented. Additionally, numerical simulations were used to investigate the degree to which heart rate variability affects the reconstruction process. Metric optimized gating enables imaging with conventional phase-contrast magnetic resonance imaging sequences in the absence of a gating signal, permitting flow measurements in the great vessels in utero.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".