A Forward Model for Estimating Phase Aberration in Ultrasound Imaging
Bibliographic record
Abstract
Phase aberration in ultrasound images is caused by inaccurate information in the sound speed distribution in the medium and can result in image distortion, such as shape change and position shifting of the imaged objects. Various methods, including cross-correlation-based methods, have been applied to the distorted images to estimate phase aberration. In this paper, we first propose that the position shifting induced by the phase aberration causes the estimated phase aberration to be inaccurate. Then, we propose an equation relating the estimated phase aberration to the true one and the equations to predict position shifting. Finally, we present a forward model for estimating phase aberration. Considering phase aberration as a function of the array element position, the theory shows that both the constant term and linear term of the true phase aberration will be canceled in the estimated phase aberration as they result solely in predictable position shifting. Field II simulations and data from tissue-mimicking phantom were used to validate the proposed theory. The theory was also applied to improve the estimation of the initial delay of ultrasound probes in both Field II simulations and experimental phantom study. Other potential applications of the proposed theory were also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".