Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)
Bibliographic record
Abstract
Abstract Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculature in vivo with a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conducted in silico using the SIMUS simulation software in an anatomically realistic phantom and then compared to in vivo acquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6 μ m and 9.93 μ m for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".