Ultrasound Localization Microscopy of the Brain: The Missing Micro Vasculature
Bibliographic record
Abstract
Ultrasound Localization Microscopy (ULM) is a powerful technique able to realize the brain microvasculature down to the micron level. Despite recent developments into retrieving more functional information (i.e., pulsatility measurements, blood flow velocity, and neurovascular coupling), there is still a need for biomarkers with high clinical significance. Capillaries, the microvascular unit at the interface between neurons and the circulatory system, may be important biomarkers for neural health. However, ULM in its current iteration cannot readily, with confidence, proclaim to capture capillary dynamics. Thus, this paper aims to explore the question, where are capillaries in ULM? We propose a solution involving long ensemble lengths for input into spatiotemporal singular value decomposition clutter filters to recover velocity profiles that exhibit capillary flow behavior. We show that with longer ensembles, we can retrieve more capillary units than standard implementations.
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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".