Frequency-locked pulse sequencer for high-frame-rate monochromatic tissue motion imaging
Bibliographic record
Abstract
To overcome the inherent low frame rate of conventional ultrasound, we have previously presented a system that can be implemented on conventional ultrasound scanners for high-frame-rate imaging of monochromatic tissue motion. The system employs a sector subdivision technique in the sequencer to increase the acquisition rate. To eliminate the delays introduced during data acquisition, a motion phase correction algorithm has also been introduced to create in-phase displacement images. Previous experimental results from tissue- mimicking phantoms showed that the system can achieve effective frame rates of up to a few kilohertz on conventional ultrasound systems. In this short communication, we present a new pulse sequencing strategy that facilitates high-frame-rate imaging of monochromatic motion such that the acquired echo signals are inherently in-phase. The sequencer uses the knowledge of the excitation frequency to synchronize the acquisition of the entire imaging plane to that of an external exciter. This sequencing approach eliminates any need for synchronization or phase correction and has applications in tissue elastography, which we demonstrate with tissue-mimicking phantoms.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".