Superharmonic Contrast Imaging with Dual-Frequency Technology for Tumor Vasculature Visualization
Bibliographic record
Abstract
Tumor vasculatures lack the hierarchy and structure of healthy tissues thus vascular morphology can be a useful indicator of the tumor evolution stages for lesion identification and cancer staging. Visualization of tumor vasculatures is therefore necessary to help the monitoring of the tumor progression and evaluate the potential efficacies of tumor treatments. Superharmonic imaging (SpHI), using the dual-frequency technology, is an imaging technique that allows high-contrast and high-resolution vasculature visualization by taking advantage of the unique nonlinear behavior of the microbubble (MB) contrast agents while suppressing the background tissue clutter. In SpHI, dual-frequency (DF) transducers are used to insonify MBs using a low-frequency (LF) excitation pulse, and capture the higher order harmonics of the broadband signature MB responses using a high-frequency (HF) receiver. Since the sum of the amplitude of the higher harmonics predominates over that of the fundamental and the second harmonics, receiving only the higher harmonics allows better signal-to-noise ratio for MBs and inherently removes tissue clutters. Thanks to the sufficient bandwidth separation between the LF and HF transducers of the DF probe, SpHI can receive up to and beyond the 10th harmonic and allows high-contrast vasculature visualizations to complement other angiography imaging tools. In this work, we introduce an array-based DF transducer that has an HF array (256 elements; 21 MHz) stacked on a LF array (32 elements; 2 MHz). We managed to control both arrays using programmable ultrasound systems to allow beamforming with electronic delays to manipulate beam shapes. We then characterized the acoustic performance of this integrated probe and demonstrated its capability of SpHI with conventional line-by-line imaging in vitro and in vivo. With more advanced beamformers, we implemented ultrafast imaging techniques for SpHI and achieved acquisition frame rates close to ~400 Hz, move SpHI towards 2D real-time acquisition while maintaining the high-contrast and high-resolution visualizations of both the vasculature and the tissue structure surrounding it. We also investigated size-selected MBs in SpHI and compared superharmonic signal intensity levels to that of commercial polydisperse MBs, and found the commercial polydisperse agents more suitable for SpHI with the established imaging parameters.
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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) | 0.000 | 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 | 0.001 |
| 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".