Optimal location of pulse compression in coded excitation for medical ultrasound imaging
Bibliographic record
Abstract
Coded excitation methods can enhance signal-to-noise ratio (SNR) in medical ultrasound imaging. Specifically, the location of pulse compression determines the computational complexity and image quality. Thus, the optimal location of pulse compression is important for coded excitation-based medical ultrasound imaging systems. In this paper, to determine the optimal location of pulse compression, the axial resolution with three different types of codes (i.e. Barker, Golay, and weighted Chirp) was examined. The pulse compression filter can be applied to various locations, i.e., behind analog-to-digital converter (ADC), beamformer (BF), quadrature demodulator (QD). In the Field II simulation, the codes (i.e., Barker, Golay and weighted Chirp with 13, 16 and 16 cycles, respectively) are used. On the other hand, for phantom experiments, pre-beamformed RF data from Barker and Golay codes were captured by a 4-MHz convex probe connected to a commercial ultrasound machine with a research package (SonixTouch, Ultrasonix, Vancouver, BC, Canada.) For all cases, the best image quality was given when pulse compression was placed behind ADC, but it is difficult to be implemented due to high hardware complexity. Behind BF and QD, for Barker and Golay, the degradation in axial resolution was observed due to nonlinear sampling during dynamic receive focusing. This degradation was minimal for Golay. On the other hand, Chirp is robust to the nonlinear sampling artifact regardless the location of pulse compression. Thus, the optimal location of pulse compression for Chirp is the behind beamformer that yields the smaller hardware complexity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".