Correlation-based ultrasound imaging: A diagnostic enabler
Bibliographic record
Abstract
Correlation-Based (CB) ultrasound imaging relies on the correlation of measured signals with signals from a simulated or measured database. The CB imaging algorithm Excitelet has demonstrated better ultrasound image quality in Non-Destructive Testing (NDT) applications. In medical applications, ultrasound imaging is characterized by weak image dynamics when compared with Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). However, portable, low-cost, and reliable diagnostic tools based on ultrasound imaging could help reducing the delays between patient assessment and treatment. In order to see more ultrasound-based diagnostics, collective research efforts are still required.Recent work on the use of the CB framework in medical imaging has enabled new diagnostic modalities. Indeed, CB imaging extends the field of view due to the near perfect compensation of the intrinsic properties of the transducers (directivity, dynamics, imperfections, lenses). New automated tools allow using a measured signal database, as opposed to simulated database previously required in CB imaging. Due to its frequency formulation, a realistic signal database enables local acoustical impedance estimation through the reconstruction of the reflectors/diffusers local phase. It is herein proposed to overlay this color-coded phase information, as typically done in Doppler imaging, to enrich the ultrasound image, and facilitate image interpretation.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".