Resolving in-vivo flow fields in the systemic circulation of the mouse through combined ultrasound imaging and computational fluid dynamics
Bibliographic record
Abstract
The "natural history" of aortic aneurysm formation and growth is not fully understood. Mouse models are most suitable to unravel the potential role of biomechanical factors (such as magnitude and patterns of wall shear stress), which are thought to interplay with vascular biology. The objective of this study is to explore whether high-frequency ultrasound imaging, combined with computational fluid dynamics (CFD), allows to resolve the flow field in the murine arterial vasculature. Ultrasound data were gathered in 10 male mice with a high-frequency ultrasound apparatus (Vevo 2100, Visualsonics, Toronto, Canada) equipped with a linear array probe (MS 550D, frequency 22-55 MHz). 3D digital models of the aorta were obtained from contrast-enhanced muCT images. Using the PW Doppler data as boundary conditions at inlets and outlets of these 3D models, CFD simulations yielded 3D flow fields with a temporal resolution in the order of 0.5 ms and spatial resolution determined by the computational grid density (<<0.1 mm). This approach, integrating imaging and CFD, provides the necessary tool for longitudinal hemodynamic studies in mice, but may also provide a modeling framework for the further optimization of high-frequency vascular ultrasound.
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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 | 0.000 | 0.000 |
| 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".