Rapid slice-to-volume four-dimensional flow in pediatric congenital heart disease: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular magnetic resonance (CMR) allows cardiac hemodynamic assessment in patients with congenital heart disease (CHD). However, conventional techniques are time-consuming and may require blood contrast agents. Slice-to-volume reconstruction (SVR) four-dimensional (4D) flow is an innovative imaging technique that may overcome these limitations. This study aimed to assess the feasibility of SVR 4D flow in pediatric CHD. METHODS: Patients with CHD (n=7, age=12.9±2.8years) underwent CMR with conventional two-dimensional (2D) phase-contrast magnetic resonance imaging (2D PCMRI) and SVR 4D flow. SVR 4D flow datasets were reconstructed from multi-slice 2D spiral PCMRI acquisitions, which were combined via slice-to-volume reconstruction. Mean flows in major thoracic vessels were measured and compared between the two techniques. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for each participant and compared between imaging techniques. RESULTS: =0.95). The SNR and CNR did not differ significantly between 2D PCMRI and SVR 4D flow data (SNR: p=0.85, CNR: p=0.90). CONCLUSION: Our results suggest that SVR 4D flow CMR is a feasible 5-minute scan (relative to multiple 2D PCMRI prescriptions and scans) in pediatric patients with CHD. SVR 4D flow showed good agreement with 2D PCMRI for mean flow measurements. The advantages of SVR 4D flow support further research such as its comparison with conventional 4D flow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".