Synthesizing Ultrasound B-mode Images from Subsampled RF Data: A Data-Driven Deep Learning Approach <sup>*</sup>
Bibliographic record
Abstract
Ultrasound, a widely used and safe imaging modality, utilizes radio frequency (RF) signals obtained from pulse-echo imaging to generate B-mode images, offering a visual representation of internal body organs and tissue microstructures. The processing details and the specific parameters applied for converting RF data to B-mode images in ultrasound devices are typically not made available by manufacturers. In this study, we investigated a convolutional network architecture for conversion of RF data into two different types of B-mode images generated by the Ultrasonix scanners. Additionally, we assessed the network's efficacy in translating subsampled RF data with fewer scan lines into the B-mode images, aiming to expedite the data acquisition and transmission process. Our results on an unseen test set demonstrate the feasibility of reconstructing B-mode images from full and subsampled RF frames with a quality similar to the scanner-generated B-mode images, even when these images undergo multiple nonlinear processing steps. The proposed approach for B-mode image reconstruction from subsampled RF frames permits scanning wider lateral field of view with the same frame rate, offering increased imaging efficiency while maintaining comparable image quality. In addition, it provides a practical solution for reconstructing high-quality B-mode images from engineered RF data in research settings, where scanner processing details are unavailable.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".