Live color encoded speckle imaging platform for real-time complex flow visualization <i>in vivo</i>
Bibliographic record
Abstract
Complex flow patterns carry valuable diagnostic information but they cannot be visualized in a time-resolved and intuitive manner in real-time. In this work, we present the first real-time scanning platform for a high frame rate ultrasound technique called color encoded speckle imaging (CESI) and its use in visualizing blood flow dynamics in vivo. CESI visualizes these dynamics through duplex rendering of flow speckle motion and color-coded Doppler velocity estimates. Its live implementation was achieved by integrating a 192-channel programmable ultrasound front-end module, a 5 GB/s capacity data streaming link, and a series of computing kernels implemented on the graphical processing unit (GPU) for beamforming, Doppler processing and display rendering. A slow-motion replay mode was also included to offer coherent visualization of CESI frames acquired at sub-millisecond resolution (0.3 ms). The live CESI scanning platform was found to be effective in facilitating real-time image guidance (>20 fps). In vivo pilot trials also showed that live CESI, when operating in replay mode, can temporally resolve the formation and dissipation of recirculation at the carotid bifurcation and can reveal flow dynamics in the brachial vein during a fist-clenching maneuver. Overall, live CESI has potential for use in routine in vivo investigations that seek to identify complex flow dynamics in real-time and relate these dynamics to vascular pathophysiology.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".