Plane wave approaches with dual-frequency arrays for superharmonic contrast imaging
Bibliographic record
Abstract
Superharmonic imaging (SpHI) using dual-frequency probes enables high-contrast microvasculature imaging by taking advantage of higher order harmonics of the broadband nonlinear response from microbubble (MB) contrast agents. We previously introduced a DF probe with a low-frequency (LF, 2 MHz; 32 elements) array behind a high-frequency (HF, 21 MHz; 256 elements) array and demonstrated SpHI with conventional walking-aperture approaches which limit acquisition rates. In this work, ultrafast imaging is investigated to overcome this challenge. We demonstrate SpHI with plane waves and coherent compounding in vitro and in vivo while evaluating acquisition frame rates. LF plane waves were implemented on VevoF2 systems (FUJIFILM Visualsonics, Toronto) with beam steering enabled by element-specific delays (9 angles between ±10°, step: 2.5°). All SpHI images showed almost complete suppression of tissue clutter to the background noise level. A 2.5 dB contrast improvement was found in vitro with coherent compounding . Tumor perfusion and fine vascular structures were visualized in vivo. SpHI acquisition frame rate reached 3.5 kHz at 0° and 396 Hz with 9 angles, ∼40 times that of walking-aperture approaches. These results demonstrate plane wave imaging approaches can increase SpHI frame rates while maintaining a high image contrast for visualizing vasculature, enabling SpHI for fast flow imaging.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".