Phase-factor based beamforming to improve the visualization of hyper-echoic targets
Bibliographic record
Abstract
Clear visualization of hyper-echoic targets such as bone surfaces in ultrasound imaging is desirable for accurate registration of ultrasound to other imaging modalities or statistical models. Challenges such as strong reverberation, off-axis reflections and speed-of-sound variation reduce the contrast and resolution of the targets. In this paper, we propose a phase-factor based beamforming method which applies the Hilbert transform on delay compensated channel data across the receive aperture. The accumulated phase is then calculated and utilized as the weight in the beamforming output. Using this method, the reverberation artifact of a point target is reduced about 9 dB compared with other beamforming methods such as delay and sum, Wiener, minimum variance and coherent-factor based beamforming in a point target phantom study. The resolution and contrast of hyper-echoic targets are also improved in a vertebrae phantom study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".