In Vivo Nanobubble-Enhanced Vascular Imaging with High-Frame-Rate Plane-Wave Ultrasound
Bibliographic record
Abstract
Nanobubbles (NBs) have emerged as promising ultrasound contrast agents for enhancing vascular and extravascular imaging. This work presents the first in vivo demonstration of NB-enhanced vascular ultrasound imaging in a preclinical setting, achieved by combining plane-wave imaging (PWI) with single-pulse transmission and high-frame-rate acquisition. The method was implemented on the VisualSonics Vevo F2 system operated in Vevo Advanced Data Acquisition (VADA) mode, which enables full customization of pulse sequences and access to raw radiofrequency (RF) data. Two techniques, adjacent frame differencing (AFD) and singular value decomposition (SVD), were applied to extract NB responses from tissue clutter. While SVD yielded stronger clutter suppression after breathing cycle segmentation, it was computationally expensive. When the frame rate was sufficiently high, AFD achieved comparable performance to SVD with significantly faster processing, making it more suitable for real-time implementation. In vivo studies on mouse kidneys demonstrated vascular visualization within seconds after NB injection. Additional studies on mouse liver and transcranial brain imaging further showcased the framework's robustness and generalizability. Results show that high-frame-rate PWI effectively mitigates motion artifacts without requiring multi-pulse transmission, such as amplitude modulation (AM) or pulse inversion (PI), to enhance nonlinear contrast. This enables robust power Doppler visualization of NB responses and flow dynamics within the vasculature. Overall, this work establishes a foundation for in vivo NB-enhanced vascular ultrasound imaging and provides strong potential for real-time preclinical applications and future clinical translation.
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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 | 0.000 |
| 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".