Novel ultrasound transmission and reconstruction techniques for synthetic transmit aperture imaging
Bibliographic record
Abstract
This dissertation describes ultrasound algorithms developed for synthetic transmit aperture (STA) imaging during the transmission and the image reconstruction stages. Images generated using these algorithms demonstrate image quality enhancement both theoretically and experimentally. The advanced algorithms also improve the application of STA imaging. Due to the single element transmission pattern, the low signal-to-noise ratio is a major limitation for STA imaging. A delay-encoded transmission scheme (DE-STA) was designed in this dissertation to encode all the transmissions. The decoded RF signals were equivalent to the standard STA signals, but with a higher SNR. Improved image qualities were observed under DE-STA transmission in terms of lateral resolution (+28%), peak-signal-to-noise ratio (PSNR, +7 dB) and target contrast-to-noise ratio (CNR, +360%) compared to those acquired with the standard STA mode. The stability of DE-STA was analyzed and verified under various noise levels by the special distribution of the singular values of the encoding matrix through singular value decomposition (SVD) (i.e. all the singular values were the same except for the first one and the last one). A more efficient decoding process was also derived based on pseudo-inversion (PI) and the computation complexity was reduced by 2/3. Speckle and undesired sidelobe signals can reduce the lesion CNR and detectability in ultrasound images. Typically, the CNR can be increased by spatial compounding (SC) or frequency compounding (FC) during reconstruction. We proposed methods to implement a 2-dimentional (2-D) aperture domain filter in the SC/FC processes, referred to as filtered spatial compounding (FSC) and filtered frequency compounding (FFC), for synthetic transmit aperture (STA) imaging. Both techniques reduced the sidelobe interference and provided improved lesion CNR. Consequently, the lesion signal-to-noise ratio (lSNR) in FSC and FFC increased (up to +130%), compared to that in the standard delay-and-sum (DAS) method. This dissertation investigates all these proposed advanced ultrasound algorithms, with the end goal of implementing these methods in STA imaging to extend its application in clinic.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".