Superharmonic and Microultrasound Imaging With Plane Wave Beamforming Techniques
Bibliographic record
Abstract
Superharmonic contrast imaging (SpHI) suppresses tissue clutter and allows high-contrast visualization of the vasculature. An array-based dual-frequency (DF) probe has been developed for SpHI, integrating a 21-MHz, 256-element microultrasound imaging array with a 2-MHz, 32-element array to take advantage of the broadband nonlinear responses from microbubble (MB) contrast agents. In this work, ultrafast imaging with plane waves was implemented for SpHI to increase the acquisition frame rate. Ultrafast imaging was also implemented for microultrasound B-mode imaging (HFPW B-mode) to enable high-resolution visualization of the tissue structure. Coherent compounding was demonstrated in vitro and in vivo in both imaging modes. Acquisition frame rates of 4.5 kHz and 187 Hz in HFPW B-mode imaging were achieved for imaging up to 21 mm with one and 25 angles, respectively, and 3.5 kHz and 396 Hz in the SpHI mode with one and nine coherently compounded angles, respectively. SpHI images showed suppression of tissue clutter prior to and after the introduction of MBs in vitro and in vivo. The nine-angle coherently compounded 2-D SpHI images of contrast-filled flow channel showed a contrast-to-tissue ratio (CTR) of 26.0 dB, a 2.5-dB improvement relative to images reconstructed from 0° steering. Consistent with in vitro imaging, the nine-angle compounded 2-D SpHI of a Lewis lung cancer tumor showed a 2.6-dB improvement in contrast enhancement, relative to 0° steering, and additionally revealed a region of nonviable tissue. The 3-D display of the volumetric SpHI data acquired from a xenograft mouse tumor using both 0° steering and nine-angle compounding allowed the visualization of the tumor vasculature. A small vessel visible in the compounded SpHI image, measuring around [Formula: see text], is not visualized in the 0° steering SpHI image, demonstrating the superiority of the latter in detecting fine structures within the tumor.
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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) | 0.001 | 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 | 0.000 |
| 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".