Multidimensional fetal flow imaging with cardiovascular magnetic resonance: a feasibility study
Bibliographic record
Abstract
<p>Purpose: To image multidimensional flow in fetuses using golden-angle radial phase contrast cardiovascular magnetic resonance (PC-CMR) with motion correction and retrospective gating.</p> <p>Methods: A novel PC-CMR method was developed using an ungated golden-angle radial acquisition with continuously incremented velocity encoding. Healthy subjects (n=5, 27 +/- 3 years, males) and pregnant females (n=5, 34 +/- 2 weeks gestation) were imaged at 3T using the proposed sequence. Real-time reconstructions were first performed for retrospective motion correction and cardiac gating (using metric optimized gating, MOG). CINE reconstructions of multidimensional flow were then performed using the corrected and gated data.</p> <p>Results: In adults, flows obtained using the proposed method agreed strongly with those obtained using a conventionally gated Cartesian acquisition. Across the five adults, bias and limits of agreement were -1.0 cm/s and [-5.1, 3.2] cm/s for mean velocities and -1.1 cm/s and [-6.5, 4.3] cm/s for peak velocities. Temporal correlation between corresponding waveforms was also high (R similar to 0.98). Calculated timing errors between MOG and pulse-gating RR intervals were low (similar to 20 ms). First insights into multidimensional fetal blood flows were achieved. Inter-subject consistency in fetal descending aortic flows (n=3) was strong with an average velocity of 27.1 +/- 0.4 cm/s, peak systolic velocity of 70.0 +/- 1.8 cm/s and an intra-class correlation coefficient of 0.95 between the velocity waveforms. In one fetal case, high flow waveform reproducibility was demonstrated in the ascending aorta (R = 0.97) and main pulmonary artery (R = 0.99).</p> <p>Conclusion: Multidimensional PC-CMR of fetal flow was developed and validated, incorporating retrospective motion compensation and cardiac gating. Using this method, the first quantification and visualization of multidimensional fetal blood flow was achieved using CMR.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".