Bibliographic record
Abstract
This dissertation describes ultrasound algorithms developed on 2D/3D synthetic transmit aperture (STA) imaging. They demonstrate significant reduction in the hardware complexity of the 2D/3D STA ultrasound imaging system while maintaining the reasonable image quality. A large-pitch synthetic transmit aperture (LPSTA) method integrated with a spatial response function (SRF) was designed in this dissertation. The LPSTA demonstrated the better lateral resolution (+25%), contrast-to-noise ratio (CNR) (+24.6%) and contrast ratio (CR) (+42.3%) than B-mode with the similar hardware complexity. LPSTA with 15-fold reduction in number measurement channels (the product of number of transmission events and the number of digital receive channels) achieved comparable image contrast to the standard STA with a full array at the cost of a reduced field of view. We extended the LPSTA method to 2D matrix array for 3D imaging, referred to as 3D-LPSTA system. A new Gaussian approximated SRF (G-SRF) was derived and integrated in the image reconstruction process to significantly improve the image contrast. With approximately 1900-fold reduction in number of measurement channels, 3D-LPSTA can provide image contrast at the specific region of interest (ROI) comparable to the standard 3D-STA with a full array and significantly better than a periodically sparse array with similar complexity. In addition to reducing the system complexity, the 3D-LPSTA achieve 700-fold reduction in computational complexity and 523-fold reduction in data storage. We proposed to combine the LPSTA with micro-beamforming technique to focus in a ROI: focused transmission and focused receiving (XTXR). The proposed XTXR method showed the image quality comparable to the standard STA a full array at the selected ROI. Moreover, the proposed XTXR method demonstrated significantly superior image quality compared to the conventional B-mode imaging with the similar micro-beamforming configuration. Finally, the beam pattern analysis was used to estimate the lateral FOV of XTXR and demonstrated a good agreement with the experimental measurement. This dissertation investigates all these proposed advanced ultrasound algorithms, with the goal of implementing these methods to extend its application in clinics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".