Comprehensive motion‐compensated highly accelerated 4D flow MRI with ferumoxytol enhancement for pediatric congenital heart disease
Bibliographic record
Abstract
PURPOSE: To develop and evaluate motion-compensation and compressed-sensing techniques in 4D flow MRI for anatomical assessment in a comprehensive ferumoxytol-enhanced congenital heart disease (CHD) exam. MATERIALS AND METHODS: A Cartesian 4D flow sequence was developed to enable intrinsic navigation and two variable-density sampling schemes: VDPoisson and VDRad. Four compressed-sensing methods were developed: A) VDPoisson scan reconstructed using spatial wavelets; B) added temporal total variation to A; C) VDRad scan using the same reconstruction as in B; and D) added motion compensation to C. With Institutional Review Board (IRB) approval and Health Insurance Portability and Accountability Act (HIPAA) compliance, 23 consecutive patients (eight females, mean 6.3 years) referred for ferumoxytol-enhanced CHD 3T MRI were recruited. Images were acquired and reconstructed using methods A-D. Two cardiovascular radiologists independently scored the images on a 5-point scale. These readers performed a paired wall motion and functional assessment between method D and 2D balanced steady-state free precession (bSSFP) CINE for 16 cases. RESULTS: Method D had higher diagnostic image quality for most anatomical features (mean 3.8-4.8) compared to A (2.0-3.6), B (2.2-3.7), and C (2.9-3.9) with P < 0.05 with good interobserver agreement (κ ≥ 0.49). Method D had similar or better assessment of myocardial borders and cardiac motion compared to 2D bSSFP (P < 0.05, κ ≥ 0.77). All methods had good internal agreement in comparing aortic with pulmonic flow (BA mean < 0.02%, r > 0.85) and compared to method A (BA mean < 0.13%, r > 0.84) with P < 0.01. CONCLUSION: Flow, functional, and anatomical assessment in CHD with ferumoxytol-enhanced 4D flow is feasible and can be significantly improved using motion compensation and compressed sensing. J. Magn. Reson. Imaging 2016;43:1355-1368.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".