Anatomically realistic simulation framework for ultrasound localization microscopy
Bibliographic record
Abstract
Ultrasound Localization Microscopy (ULM) can map vessels at the capillary scale (<10 μm) by acquiring tens of thousands of images in which injected microbubbles are detected and tracked to generate super-resolved blood vessel maps. However, to our knowledge, there are no validation frameworks for ULM image formation algorithms. Herein, we developed a 3-D, anatomically realistic simulation tool based on serial two-photon microscopy (STPM). Rodent brain vasculature was segmented from STPM [1] into a graph-based model, which was then used to generate microbubble flow trajectories according to a velocity-diameter relationship derived from in vivo ULM data [2]. Ultrasound signals were obtained using a fast GPU-based simulator and reconstructed using our ULM image formation algorithm. We then proceeded to a parametric study. We first compared 2-D with 3-D imaging. Despite the latter's lower contrast, the gain in spatial information led to a more complete vasculature map. Localization precision was also enhanced since it was freed from the intrinsic localization error in the elevation direction. Also, increasing the number of transmits enhanced localization precision. From this study, we could establish a relationship between microbubble concentration and acquisition time for full network filling and optimal localization. This simulation tool provides thus a ground truth for the validation of ULM image formation algorithms and enables testing of new hypotheses. [1] R. Damseh et al., IEEE JBHI(2018). [2] V. Hingot et al., Sci Rep. (2019).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".