A convolutional neural network approach to preserve image quality for sparse array data
Bibliographic record
Abstract
High-frame-rate ultrasound (HiFRUS) has seen recent interest in resolving highly dynamic spatiotemporal events but transferring the large amount of data generated from the probe remains a hurdle for real-time imaging. One method to lessen the data traffic while preserving the field of view is to reduce the channel count, but this can lead to image quality degradation and the appearance of grating lobe artifacts. In this work, we present a convolutional neural network (CNN) based framework that uses a sparse array (half the channel count) and infers the remaining channels to mimic a fully-populated array. On unfocused transmissions, our results show that on a beamformed image of a multiple point target phantom, grating lobe artifacts are reduced from over 8dB (sparse array) to less than 1 dB (CNN interpolated) when compared to an image beamformed using the full array. Additionally, reconstructions from CNN generated data demonstrated improvement (10 dB) in carotid echolucent flow regions in vivo. Our work demonstrates that, using a deep learning approach to channel-domain radiofrequency data interpolation, the required physical channel count on an array and the corresponding data transfer bandwidth can both be reduced without significant image quality trade-off.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".