Evaluation of a fractional filter-based receive beamforming method for ultrasound color Doppler imaging
Bibliographic record
Abstract
In medical ultrasound imaging, dynamic receive beamforming has been used for improving signal-to-noise ratio (SNR) and spatial resolution. For low-cost portable ultrasound imaging systems, a fractional filter-based receive beamforming (FFRB) method was previously proposed to reduce the hardware complexity compared to conventional interpolation filter-based receive beamforming methods (IFRB). While this new beamforming method substantially reduces the hardware complexity, it yields the nonlinear phase response for high frequencies due to the limited length of fractional filter coefficients, leading to the bias on flow estimation in ultrasound color Doppler imaging. In this paper, to evaluate the FFRB method for ultrasound color Doppler imaging, the Field II simulation and string phantom experiments were conducted. In Field II simulation, the radio-frequency (RF) data were generated by assuming a 7.5-MHz linear array probe with the transmit frequency of 6 MHz, the ensemble size of 8, and the sampling frequencies of 20 MHz. In string phantom experiments, the RF channel data were obtained with a commercial SonixTouch ultrasound scanner equipped with a research package (Ultrasonix Corp., Vancouver, BC, Canada) and a 5-MHz linear array connected to a SonixDAQ parallel system. The ensemble size and the sampling frequency were set to 10 and 20 MHz, respectively. For the Field II simulation and string phantom experiments, only 1.2% and 2.3 % in color Doppler estimation error ratio was observed with mean and standard deviation along the lateral direction. This result indicates that the proposed FFRB method could be utilized for a low-cost ultrasound color Doppler imaging system with lowered hardware complexity and minimized phase errors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".