Ultrasound super resolution imaging of nanodroplets with a multi-frequency hemispherical phased array
Bibliographic record
Abstract
High resolution imaging of microvasculature is desirable for diagnostic and therapeutic applications in the brain. Here, we investigated the use of a 256-module sparse hemispherical transducer array to map the emissions of lipid-coated Decafluorobutane nanodroplets (∼210 ± 80 nm, 107–109 droplets/ml) flowing through tube phantoms (0.8 mm inner diameter). Each array module comprised 4 concentric cylindrical PZT-4 elements (55/306/612/1224 kHz). Droplets were vaporized at 55 kHz (0.10–0.18 MPa, 145 μs bursts every 2 s) and the resulting emissions were received on either the 306, 612 or 1224 kHz subarrays. Low-resolution 3-D images were formed using delay-and-sum passive beamforming, and super-resolved images were obtained via Gaussian fitting of the estimated point-spread-function to the low-resolution data. With super-resolution techniques, the mean lateral (axial) full-width-at-half-maximum image intensity was 35 ± 6 (67 ± 11), 16 ± 3 (32 ± 6), and 7 ± 1 (15 ± 2) μm from 160 (2970), 241 (2970), and 117 (4950) vaporization events (total frames), corresponding to ∼1/85 of normal resolution at 306, 612 and 1224 kHz, respectively. The mean positional uncertainties were ∼1/350 (lateral) and ∼1/180 (axial) of the receive wavelength in water. The pressure threshold for vaporization detection increased with increasing receive frequency. This study demonstrates the feasibility of mapping vaporized nanodroplets with passive beamforming and super-resolution imaging techniques.
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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 | 0.000 |
| 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) | 0.001 | 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".