Compressibility and Density Weighting for Ultrasound Scattering Tomography
Bibliographic record
Abstract
A novel reconstruction technique based on delay-and-sum ultrasound tomography algorithms is introduced. This reconstruction technique enables ultrasound tomographic systems to produce compressibility- and density-weighted images (D-WI) for spherically symmetric ultrasound scatterers. The efficacy of this reconstruction technique was demonstrated with simulation and phantom experiments. Separation between a dense wire target and a compressible thread target within a phantom was quantified, showing an increased signal of the wire target for D-WIs. The suppression of background scatter in D-WIs was also quantified. Resolution was calculated for these reconstruction techniques, exemplifying the half-wavelength diffraction-limited resolution capabilities of this ultrasound scattering tomography system. The proposed technique offers a enhanced minimum-detectable density-contrast sensitivity compared to traditional B-scan imaging for a 100- target. This enhanced detectability is expected to prove advantageous for microcalcification imaging.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".