Adaptive sound speed correction for abdominal ultrasonography: preliminary results
Bibliographic record
Abstract
Ultrasonography has been conducting a critical role in assessing abdominal disorders due to its noninvasive, real-time, low cost, and deep penetrating capabilities. However, for imaging obese patients with a thick fat layer, it is challenging to achieve appropriate image quality with a conventional beamforming (CON) method due to phase aberration caused by the difference between sound speeds (e.g., 1580 and 1450m/s for liver and fat, respectively). For this, various sound speed correction (SSC) methods that estimate the accumulated sound speed for a region-of interest (ROI) have been previously proposed. However, with the SSC methods, the improvement in image quality was limited only for a specific depth of ROI. In this paper, we present the adaptive sound speed correction (ASSC) method, which can enhance the image quality for whole depths by using estimated sound speeds from two different depths in the lower layer. Since these accumulated sound speeds contain the respective contributions of layers, an optimal sound speed for each depth can be estimated by solving contribution equations. To evaluate the proposed method, the phantom study was conducted with pre-beamformed radio-frequency (RF) data acquired with a SonixTouch research package (Ultrasonix Corp., Canada) with linear and convex probes from the gel pad-stacked tissue mimicking phantom (Parker Lab. Inc., USA and Model539, ATS, USA) whose sound speeds are 1610 and 1450m/s, respectively. From the study, compared to the CON and SSC methods, the ASSC method showed the improved spatial resolution and information entropy contrast (IEC) for convex and linear array transducers, respectively. These results indicate that the ASSC method can be applied for enhancing image quality when imaging obese patients in abdominal ultrasonography.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".