Bibliographic record
Abstract
Ultrasound localization microscopy (ULM) can map the vasculature at large depth with unprecedented resolution by localizing millions of injected microbubbles in hundreds of thousands of images acquired over a few minutes. The current state of the art in ULM is to use low concentrations to achieve the best possible spatial resolution without providing temporal information, which limits the development of functional biomarkers such as pulsality or the imaging of moving organs like the heart. In this work, we will present dynamic ultrasound localization microscopy (DULM), which enables the generation of dynamic images of the vasculature of periodic phenomena by using a combination of enhanced image formation and processing techniques to drastically increase the number of microbubbles that can be detected in each image. Specifically, we will describe how the detection of microbubbles directly in space time along with novel aberration correction algorithms and a motion-invariant Lagrangian beamforming approach can be used to increase the concentration of microbubbles 5-fold with a limited degradation in resolution. Examples of application for the mapping of pulsatility in the brain and the dynamics of the intramyocardial blood flow in 2D + t and 3D + t will be shown.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".