Ultrasonic b-scan imaging with adaptive beamformation using aberration correction
Bibliographic record
Abstract
The effectiveness of adaptive beam formation using aberration correction has been demonstrated in ultrasonic b-scans of liver-mimicking scattering phantoms imaged with and without an intervening aberrator that produced wavefront distortion comparable to that of abdominal wall. Images of 4 mm diam spherical features (either positive or negative contrast lesions or scatterer-free cysts) in the uniform scattering background of the phantoms were produced at 3.0 MHz with a two-dimensional (80×80-element) array transducer system. Time-shift aberration was estimated from the scattering data and used to compensate both transmit and receive waveforms. Image improvements were assessed by comparison of feature contrast with and without aberration correction in individual images and by comparison of intensities in averages of independent, statistically identical images. Feature contrasts and borders were visibly and measurably improved, sometimes to near the water path results, using aberration correction, particularly when both transmit and receive corrections were applied. An efficient implementation of aberration correction was achieved by correction of multiple image scan lines with a single aberration estimate. Aberration correction using estimates from one-seventh the number of scan lines in 8 mm wide images produced improvements comparable to those achieved by individually estimating and correcting aberration in every scan line.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".