Comparisons of lesion detectability in ultrasound images acquired using time-shift compensation and spatial compounding
Bibliographic record
Abstract
The effects of aberration, time-shift compensation, and spatial compounding on the discrimination of positive-contrast lesions in ultrasound b-scan images are investigated using a two-dimensional (2-D) array system and tissue-mimicking phantoms. Images were acquired within an 8.8 x 12-mm2 field of view centered on one of four statistically similar 4-mm diameter spherical lesions. Each lesion was imaged in four planes offset by successive 45 degree rotations about the central scan line. Images of the lesions were acquired using conventional geometric focusing through a water path, geometric focusing through a 35-mm thick distributed aberration phantom, and time-shift compensated transmit and receive focusing through the aberration phantom. The views of each lesion were averaged to form sets of water path, aberrated, and time-shift compensated 4:1 compound images and 16:1 compound images. The contrast ratio and detectability index of each image were computed to assess lesion differentiation. In the presence of aberration representative of breast or abdominal wall tissue, time-shift compensation provided statistically significant improvements of contrast ratio but did not consistently affect the detectability index, and spatial compounding significantly increased the detectability index but did not alter the contrast ratio. Time-shift compensation and spatial compounding thus provide complementary benefits to lesion detection.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".