Coupling Fast Superresolution CNN with Fast Plane-Wave Fourier-Domain Beamforming
Bibliographic record
Abstract
Plane-wave ultrasound imaging, in conjunction with Fourier-domain receive beamforming, allows for very high rates of data acquisition and data processing. Fourier-domain reconstruction is typically coupled with postbeamforming interpolation of its output into a desired final image grid. This interpolation can be enhanced by using a fast superresolution convolutional neural network (CNN) to upscale Fourier-beamformed envelope data. We show that such an approach can produce high-quality images when a CNN is first pretrained on the diverse (non-ultrasound) 1,000-image dataset DIV2K, followed by transfer learning on a small augmented dataset of 160 ultrasound images. We generated these images from public-domain experimental data provided by the well-known PICMUS evaluation framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".