High-Frequency 4-Dimensional Ultrasound (4DUS): A Reliable Method for Assessing Murine Cardiac Function
Bibliographic record
Abstract
In vivo imaging has provided a unique framework for studying pathological progression in various mouse models of cardiac disease. Although conventional short-axis motion-mode (SAX MM) ultrasound and cine magnetic resonance imaging (MRI) are two of the most prevalent strategies used for quantifying cardiac function, there are few notable limitations including imprecision, inaccuracy, and geometric assumptions with ultrasound, or large and costly systems with substantial infrastructure requirements with MRI. Here we present an automated 4-dimensional ultrasound (4DUS) technique that provides comparable information to cine MRI through spatiotemporally synced imaging of cardiac motion. Cardiac function metrics derived from SAX MM, cine MRI, and 4DUS data show close agreement between cine MRI and 4DUS but overestimations by SAX MM. The inclusion of a mouse model of cardiac hypertrophy further highlights the precision of 4DUS compared with that of SAX MM, with narrower groupings of cardiac metrics based on health status. Our findings suggest that murine 4DUS can be used as a reliable, accurate, and cost-effective technique for longitudinal studies of cardiac function and disease progression.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".