Characterization of an Array-Based Dual-Frequency Transducer for Superharmonic Contrast Imaging
Bibliographic record
Abstract
Superharmonic imaging with dual-frequency imaging systems uses conventional low-frequency ultrasound transducers on transmit, and high-frequency transducers on receive to detect higher order harmonic signals from microbubble contrast agents, enabling high-contrast imaging while suppressing clutter from background tissues. Current dual-frequency imaging systems for superharmonic imaging have been used for visualizing tumor microvasculature, with single-element transducers for each of the low- and high-frequency components. However, the useful field of view is limited by the fixed focus of single-element transducers, while image frame rates are limited by the mechanical translation of the transducers. In this article, we introduce an array-based dual-frequency transducer, with low-frequency and high-frequency arrays integrated within the probe head, to overcome the limitations of single-channel dual-frequency probes. The purpose of this study is to evaluate the line-by-line high-frequency imaging and superharmonic imaging capabilities of the array-based dual-frequency probe for acoustic angiography applications in vitro and in vivo. We report center frequencies of 1.86 MHz and 20.3 MHz with -6 dB bandwidths of 1.2 MHz (1.2-2.4 MHz) and 14.5 MHz (13.3-27.8 MHz) for the low- and high-frequency arrays, respectively. With the proposed beamforming schemes, excitation pressure was found to range from 336 to 458 kPa at its azimuthal foci. This was sufficient to induce nonlinear scattering from microbubble contrast agents. Specifically, in vitro contrast channel phantom imaging and in vivo xenograft mouse tumor imaging by this probe with superharmonic imaging showed contrast-to-tissue ratio improvements of 17.7 and 16.2 dB, respectively, compared to line-by-line micro-ultrasound B-mode 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".