Bibliographic record
Abstract
This dissertation introduces the decorrelated compounding methods in synthetic transmit aperture (STA) ultrasound imaging and the spatial frequency domain of beamformed ultrasound images. They improve the detectability of low-contrast lesions in terms of lesion signal-to-noise ratio (lSNR), and visual detection. First, a decorrelation procedure was applied to traditional spatial and frequency compounding in STA to improve the lSNR in this dissertation. The decorrelated compounding method shown a better performance of speckle reduction than the conventional incoherent compounding methods at the cost of spatial resolution loss. The overall effect in terms of lSNR, which considered both speckle reduction and spatial resolution loss, indicated that the DC in STA outperformed the Delay-and-Sum (DAS) method. Then, we proposed to apply a two dimensional low-pass filter in the aperture domain to suppress the artifacts caused by the off-axis signals of DC in STA. In clinical applications, strong off-axis signals can be encountered such as irregular borders, calcification or development of vascularity. Both simulation and experiment images demonstrate the effectiveness of the filter. Lastly, the principle of decorrelated compounding was extended beyond STA to the any radiofrequency ultrasound images. The spatial frequency spectrum of beamformed ultrasound images was divided into overlapped sub-domains to generate sub-images for decorrelation and compounding. This method improved lSNR over the DAS method. In addition, the computational complexity was reduced by a factor of 16 compared to DC in STA. This dissertation investigate these decorrelated compounding based methods with the goal of improving the detectability of low-contrast lesions.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".