Ultrasound Image Velocimetry for High Spatiotemporal Resolution Blood Flow Velocity Field Mapping in Mice
Bibliographic record
Abstract
OBJECTIVE: Abnormal hemodynamics is thought to play an essential role in the development of cardiovascular diseases. Mouse models are widely used for elucidating the underlying mechanisms; however, their small size and high heart rates make it difficult to perform quantitative flow velocity field mapping with sufficient temporal resolution. Our objective was to develop a noninvasive method for quantitative flow field mapping in mice based on speckle-tracking from high-frequency ultrasound B-mode imaging. METHODS: Ultrasound ECG-gated kilohertz visualization (EKV) was performed on a mouse-aorta-sized tubular flow phantom at frame rates up to 10,000 fps. Unexpected velocity underestimations were elucidated by simulating EKV reconstruction and performing ultrasound image velocimetry (UIV) in silico. A technique for error correction was developed and validated in vitro, and demonstrated in vivo. RESULTS: In flow phantoms, EKV-UIV underestimated velocity in the beam lateral direction by 50%-70%. This was attributed to loss of speckle contiguity owing to EKV's retrospective strip-based reconstruction of the two-dimensional B-mode image. The proposed correction technique reduced the errors to <10% by accounting only for speckle movement within each image strip. A preliminary in vivo study showed that vortex shapes and near-wall expansion movement inside a mouse left ventricle were more aligned with physical expectations after correction. CONCLUSION: A novel technique was developed to quantitatively map blood flow with high spatiotemporal resolution. Further optimization will enable longitudinal studies in mice to gain insights on the role of local hemodynamic forces in the development of cardiovascular diseases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".