Direct spatiotemporal localization of microbubble trajectories for highly resolved hemodynamics in ultrasound localization microscopy
Bibliographic record
Abstract
Using high concentrations of microbubbles in ultrasound localization microscopy can reduce acquisition time but is also associated with reduced localization precision and accuracy of blood flow measurements. To address these limitations, we introduce Ultrasound Spatio-Temporal Localization (USTL), a novel approach that localizes microbubble trajectories using a spatiotemporal physiological constraint, in contrast to standard approaches that detect, pair, and track microbubbles over time without a priori. We tested USTL in vivo in the brains of rats and mice using a 15 MHz linear array probe and a Vantage system at different concentrations of microbubbles. Overall, USTL increased the number of detected microbubbles while reducing processing time and susceptibility to signal attenuation at depth. USTL provided coherent velocity profiles in vessels regardless of microbubble concentration, making it a valuable tool for studying brain hemodynamics in various conditions. Overall, USTL offers a new approach for the non-invasive measurement of dynamic brain function with high spatial and temporal resolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".