Bibliographic record
Abstract
Methods:The feasibility to acquire a 4D dataset of the aortic arch and to visualise fetal cardiac hemodynamics was evaluated in six fetuses (Gestation week 30 -35) and compared to a gated 2D cine phase contrast angiography sequence.Cardiac gating was performed using aMRI compatible Doppler ultrasound (DUS) device.The sequences were limited to 10 heart phases to minimise imaging time, resulting in a temporal resolution of 40ms and scan time of 2.30min.Morphologic images of the aorta and fetal heart were acquired using a retrospectively gated cine balanced steady-state free precession sequences.Visualisations and analysis were performed using Gyro Tools.Results: The aortic flow as well as flow in supra-aortic vessels could be clearly observed and their velocity could be measured applying the 4D flow measurement (figure 1).There was a partial blood turbulence in the atrium.This must be caused by the foramen ovale, which is the natural right-to-left atrial shunt.Quantitative measurements were well comparable between 4D and 2D phase contrast acquisitions for both the fetal phantom and for the human subjects.Fetal movement is a challenge due to long acquisition times and needs to be addressed in future studies.Conclusions: This preliminary study showed the possibility of visualisation and quantitative measurements of blood flow in-utero within the great fetal vessels with 4D phase contrast imaging utilizing the Doppler ultrasound gating method.The technique may be beneficial for visualisation and quantification of complex congenital cardiovascular malformations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.621 | 0.438 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".