3-D Microvascular Imaging Using High Frame Rate Ultrasound and ASAP Without Contrast Agents: Development and Initial <i>In Vivo</i> Evaluation on Nontumor and Tumor Models
Bibliographic record
Abstract
Three-dimensional imaging is valuable to noninvasively assess angiogenesis given the complex 3-D architecture of vascular networks. The emergence of high frame rate (HFR) ultrasound, which can produce thousands of images per second, has inspired novel signal processing techniques and their applications in structural and functional imaging of blood vessels. Although highly sensitive vascular mapping has been demonstrated using ultrafast Doppler, the detectability of microvasculature from the background noise may be hindered by the low signal-to-noise ratio (SNR) particularly in the deeper region and without the use of contrast agents. We have recently demonstrated a coherence-based technique, acoustic subaperture imaging (ASAP), for super-contrast vascular imaging and illustrated the contrast improvement using HFR contrast-enhanced ultrasound. In this work, we provide a feasibility study for microvascular imaging using ASAP without contrast agents, and extend its capability from 2-D to volumetric vascular mapping. Using an ultrasound research system and a preclinical probe, we demonstrated the improved visibility of microvascular mapping using ASAP in comparison to ultrafast power Doppler (PD) on a mouse kidney, liver, and tumor without contrast agent injection. The SNR of ASAP images improves in average by 10 dB when compared to PD. In addition, directional velocity mappings were also demonstrated by combining ASAP with the phase information extracted from lag-1 autocorrelation. The 3-D vascular and velocity mapping of the mouse kidney, liver, and tumor were demonstrated by stacking the ASAP images acquired using 2-D ultrasound imaging and a trigger-controlled linear translation stage. The 3-D results depicted clear microvasculature morphologies and functional information in terms of flow direction and velocity in two nontumor models and a tumor model. In conclusion, we have demonstrated a new 3-D in vivo ultrasound microvascular imaging technique with significantly improved SNR over existing ultrafast Doppler.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".