Abstract 94: Comparison of Ultrasound and Compact MRI for the Assessment of Cardiac Function in Mice
Bibliographic record
Abstract
Introduction: Ultrasound (US) is a standard for the assessment of cardiovascular function. Pre-clinically, this is due the modality’s translational potential, its high temporal resolution and the absence of infrastructure. Recent advancements in novel high-performance compact MRI has made cardiac MRI a more accessible technique for assessing a variety of pathologies in murine models of CVD while reducing the complexity and costs traditionally associated with superconducting MRI systems. These developments may allow compact MRI to overcome some of the limitations of US meanwhile producing viable solutions for quantification along with new capabilities for cardiovascular biomarker assessment. Hypothesis: This study explores the relative capabilities of novel compact high-performance MRI compared to HFU for phenotypic analysis of mouse models of CVD. Methods: Four normal and one induced myocardial infarct C57BL/6 mice were imaged using both US and MRI. MI mice were prepared by ligation of the left anterior descending artery. In order to assess the ability of each modality to qualitatively and quantitatively characterize cardiac function, CINE loops of long axis and short axis slices were acquired with both systems and analyzed offline. In addition to traditional measures of cardiac function, strain analysis was performed using the HARP method for compact MRI and using speckle tracking for US. Finally, a cardiac MRI method for mapping and measuring infarct extent using gadolinium (Gd) contrast agents is described. Results and Conclusions: Although compact MRI has lower temporal resolution, when compared with US, it still provides many advantages in pre-clinical CVD imaging. In particular, because MRI signal is not attenuated by dense tissue or air, compact MRI can successfully generate artefact-free imaging of pathologies difficult or impossible to image with US, such as clearly visualizing and quantifying endo and epicardial borders. It also provides a whole body image and a “whole heart” image, making pre-clinical CV imaging easier for biologists and helping to reduce inter-operator variability. Finally, high sensitivity to Gd-based contrast agent with compact MRI enable new applications such as infarct quantification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".