Phase Aberration Estimation in Synthetic Transmit Aperture Ultrasound Imaging and Its Application to Estimating Sound Speed
Bibliographic record
Abstract
Despite the broad application of ultrasound imaging in modern diagnostic modalities, it often suffers from suboptimal image quality. Phase aberration is one of the main contributors to image degradation, and it appears as poor contrast, poor lateral resolution, and fill-in into hypoechoic regions due to the degraded beam-focusing quality. Image reconstruction is usually performed under the assumption of a homogeneous medium. Nonetheless, in the presence of spatial sound-speed heterogeneity, this hypothesis is no longer valid and leads to errors in the estimated echo arrival time. This dissertation investigates phase aberration estimation methods in synthetic transmit aperture ultrasound imaging (STA) and its application in estimating the speed map of the medium. STA Radio-frequency (RF) data were simulated and also acquired in experiments. However, the signal-to-noise ratio (SNR) of STA signals was much lower than that in B-mode. Therefore, we first developed a Filtered-Normalized-Cross-Correlation (F-NCC) method to estimate the phase aberration in noisy STA data. A 2D filter was applied in the temporal and spatial frequency domain to reduce the noise and off-axis signals, and its performance was validated with both simulation and experiment data. Then we derived an equation to relate the phase aberration to the average speed at a point in the medium. This equation was applied to estimate the average sound speed in the medium. It was demonstrated with a two-layered phantom that the average speed map can be used to estimate the local speed in layered objects, such as in the presence of subcutaneous fat and connective tissues. The image reconstructed based on the speed map across the medium had an improved quality globally. Furthermore, the local speed could be utilized as a biomarker. Phase aberration estimation and speed map methods were more successful when they were iterated. In the above studies, the focal point was selected in the first iteration, and it was not updated later. An adaptive localization method in which the focal point was updated by choosing the maximum brightness point within the selected area in each iteration was proposed. The results of both the simulation and experimental studies showed that adaptive localization improves the phase aberration estimation by 80% and the average speed estimation by 60%. In the future work, the potential to apply the phase aberration estimation method to estimate the local speed in non-layered objects was discussed.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".