Four-dimensional Flow Magnetic Resonance Imaging Quantification of Blood Flow in Bicuspid Aortic Valve
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Four-dimensional (D) flow magnetic resonance imaging (MRI) is limited by time-consuming and nonstandardized data analysis. We aimed to test the efficiency and interobserver reproducibility of a dedicated 4D flow MRI analysis workflow. MATERIALS AND METHODS: Thirty retrospectively identified patients with bicuspid aortic valve (BAV, age=47.8±11.8 y, 9 male) and 30 healthy controls (age=48.8±12.5 y, 21 male) underwent Aortic 4D flow MRI using 1.5 and 3 T MRI systems. Two independent readers performed 4D flow analysis on a dedicated workstation including preprocessing, aorta segmentation, and placement of four 2D planes throughout the aorta for quantification of net flow, peak velocity, and regurgitant fraction. 3D flow visualization using streamlines was used to grade aortic valve outflow jets and extent of helical flow. RESULTS: 4D flow analysis workflow time for both observers: 5.0±1.4 minutes per case (range=3 to 10 min). Valve outflow jets and flow derangement was visible in all 30 BAV patients (both observers). Net flow, peak velocity, and regurgitant fraction was significantly elevated in BAV patients compared with controls except for regurgitant fraction in plane 4 (91.1±29.7 vs. 62.6±19.6 mL/s, 37.1% difference; 121.7±49.7 vs. 90.9±26.4 cm/s, 28.9% difference; 9.3±10.1% vs. 2.0±3.4%, 128.0% difference, respectively; P<0.001). Excellent intraclass correlation coefficient agreement for net flow: 0.979, peak velocity: 0.931, and regurgitant fraction: 0.928. CONCLUSION: Our study demonstrates the potential of an efficient data analysis workflow to perform standardized 4D flow MRI processing in under 10 minutes and with good-to-excellent reproducibility for flow and velocity quantification in the thoracic aorta.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it