Dynamic microvascular biomarkers in ultrasound localization microscopy: From simulation to <i>in vivo</i> application
Bibliographic record
Abstract
Ultrasound localization microscopy (ULM) offers non-invasive, deep-tissue imaging of the microvasculature by tracking millions of intravenously injected, clinically approved, individual microbubbles. In this work, we introduce a simulation–experiment framework, combining realistic vascular modeling with a large, open-access database of in vivo transcranial mouse ULM datasets that we recently released. We use this framework to develop novel biomarkers in small vessels such as pulsatility imaging, pulse-wave imaging, capillary transit time, and the detection of capillary stalls. This framework enabled the development of several technical improvement such as the track-and-localize approach and robust aberration correction. Using these improvements, we show the feasibility of mapping pulsatility and pulse-wave velocity in the entire brain in vessels as small as 30 microns in diameter. We also introduce the concept of single capillary reporters, i.e., single microbubbles that were tracked over thousands of frames from the arterial to the veinous side of the vascular tree and thus enabling the quantitative mapping of transit time and stalls in capillaries, which correlate with neuroinflammation in a LPS-challenge mouse model. Finally, we explore the translational potential of these methods in larger brains in vivo, paving the way for clinical imaging of the microvasculature.
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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 | 0.002 |
| 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 | 0.001 | 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".