Volumetric Fetal Flow Imaging With Magnetic Resonance Imaging
Bibliographic record
Abstract
Fetal development relies on a complex circulatory network. Accurate assessment of flow distribution is important for understanding pathologies and potential therapies. In this paper, we demonstrate a method for volumetric imaging of fetal flow with magnetic resonance imaging (MRI). Fetal MRI faces challenges: small vascular structures, unpredictable motion, and inadequate traditional cardiac gating methods. Here, orthogonal multislice stacks are acquired with accelerated multidimensional radial phase contrast (PC) MRI. Slices are reconstructed into flow sensitive time-series images with motion correction and image-based cardiac gating. They are then combined into a dynamic volume using slice-to-volume reconstruction (SVR) while resolving interslice spatiotemporal coregistration. Compared to prior methods, this approach achieves higher spatiotemporal resolution ($1\times 1\times 1$mm3, ~30 ms) with reduced scan time – important features for the quantification of flow through small fetal structures. Validation is demonstrated in adults by comparing SVR with 4D radial PCMRI (flow bias and limits of agreement: −1.1 ml/s and [−11.8 9.6] ml/s). Feasibility is demonstrated in late gestation fetuses by comparing SVR with 2D Cartesian PCMRI (flow bias and limits of agreement: −0.9 ml/min/kg and [−39.7 37.8] ml/min/kg). With SVR, we demonstrate complex flow pathways (such as parallel flow streams in the proximal inferior vena cava, preferential shunting of blood from the ductus venosus into the left atrium, and blood from the brain leaving the heart through the main pulmonary artery) for the first time in human fetal circulation. This method allows for comprehensive evaluation of the fetal circulation and enables future studies of fetal physiology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".