Extended Field of View Imaging Through Correlation With an Experimental Database
Bibliographic record
Abstract
In this article, a correlation-based (CB) ultrasound imaging technique is implemented to extend the field of view (FOV) in the inspected medium and to enhance image homogeneity. This implementation involves the acquisition, the compression, and the adaptation of a database of experimental reference signals (CB-Exp), consisting of backpropagated reflections on point-like scatterers at different positions, as an improvement over preceding implementations involving a database of numerical reference signals (CB-Num). Starting from a large database acquired in water to a database with a 99% size reduction that can be applied to tissue-like media, CB-Exp has been validated in vitro on a CIRS 040GSE phantom. When compared with the synthetic aperture focusing technique (SAFT) and CB-Num, CB-Exp results show reduced sensitivity to the probe's directivity, allowing an FOV extension from 25° with SAFT to 75° with CB-Exp. In vivo testing on a piglet's heart with CB-Exp imaging showed a 3.5-dB contrast improvement on the pericardium wall. Overall benefits of this method include a reduction in the background gCNR standard deviation (std) of 0.2 and a reduction in the std of 10 dB in the point-like targets levels, which translates to more homogeneous sensitivity in the axial and lateral directions of the image.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".