Multiresolution comparison of fetal real-time and cine magnetic resonance imaging at 0.55T
Bibliographic record
Abstract
BACKGROUND: Dynamic fetal cardiovascular MRI (CMR) enables visualization of moving structures to assess congenital heart disease and plan treatment. Low field MRI systems can provide more comfortable platforms for fetal CMR. Here, we demonstrate the feasibility and utility of motion corrected fetal cardiac cine CMR and compare it with real-time CMR at multiple spatial resolutions at 0.55 T. METHODS: Ten human pregnancies were scanned at 0.55T on a derated MAGNETOM Aera (Siemens Healthineers, Erlangen, Germany) with spiral steady-state free precession imaging. Real-time images were reconstructed and used for motion correction and fetal cardiac gating followed by cine reconstructions. The signal-to-noise ratio (SNR), image quality, blood-to-myocardium contrast, and contrast-to-noise ratio (CNR) from real-time and cine reconstructions were compared. The effect of acceleration on cine accuracy was assessed by retrospectively undersampling the data and measuring the reconstruction error with the normalized root-mean-squared difference (NRMSD) in five fetuses. Reproducibility of the measurements was assessed by reconstructing cines from two independent windows of data and computing the NRMSD relative to the reference image in five fetuses. RESULTS: The SNR, CNR, and image quality were better for cines than their corresponding real-time reconstructions. The blood-to-myocardium contrast had no significant difference between real-time and cine reconstructions. With finer spatial resolution, real-time images degraded, and cardiac structures were less conspicuous. NRMSD in cines decreased with increasing scan times across all resolutions (NRMSD = 10 ± 2% for 7 s scan duration). Good consistency (NRMSD = 11 ± 3%) was achieved between independent reconstruction windows. CONCLUSION: While this study was performed on an experimental scanner (derated; not commercially available), we have shown that fetal cine CMR is feasible at 0.55T and provides high-quality fetal cardiac images at high spatiotemporal resolutions.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".