Motion robust respiratory‐resolved 3D radial flow MRI and its application in neonatal congenital heart disease
Bibliographic record
Abstract
Purpose To test and implement a motion‐robust and respiratory‐resolved 3D Radial Flow framework that addresses the need for rapid, high resolution imaging in neonatal patients with congenital heart disease. Methods A 4‐point velocity encoding and 3D radial trajectory with double‐golden angle ordering was combined with bulk motion correction (from projection center of mass) and respiration phase detection (from principal component analysis of heartbeat‐averaged data) to create motion‐robust 3D velocity cardiac time‐averaged data. This framework was tested in a whole‐chest digital phantom with simulated bulk and realistic physiological motion. In vivo imaging was performed in 20 congenital heart disease infants under feed‐and‐sleep with submillimeter isotropic resolution in ~3 min. Flows were validated against clinical 2D PCMRI and whole‐heart visualizations of blood flow were performed. Results The proposed framework resolved all simulated digital phantom motion states (mean ± standard error: rotation – azimuthal = 0.29 ± 0.02°; translation – Ty = 1.29 ± 0.12 mm, Tz = −0.27 ± 0.13 mm; rotation+translation – polar = 0.49 ± 0.16°, Tx = −2.47 ± 0.51 mm, Tz = 5.78 ± 1.33 mm). Measured timing errors of peak expiration across all signal‐to‐noise ratio values were 22% of the true respiratory period (range = [404‐489 ± 298‐334] ms). For in vivo imaging, motion correction improved 3D Radial Flow measurements (no correction: R2 = 0.62, root mean square error = 0.80 L/min/m2, Bland‐Altman bias [limits of agreement] = −0.21 [−1.40, 0.94] L/min/m2; motion corrected, expiration: R2 = 0.90, root mean square error = 0.46 L/min/m2, bias [limits of agreement] = 0.06 [−0.49, 0.62] L/min/m2). Respiratory‐resolved 3D velocity visualizations were achieved in various neonatal pathologies pre‐ and postsurgical correction. Conclusion 3D cardiac flow may be visualized and accurately quantified in neonatal subjects using the proposed framework. This technique may enable more comprehensive hemodynamic studies in small infants.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".