Fast Multi-Focus Ultrasound Image Recovery Using Generative Adversarial Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In conventional ultrasound (US) imaging, it is common to transmit several focused beams at multiple locations to generate a multi-focus image with constant lateral resolution throughout the image. However, this method comes at the expense of a loss in temporal resolution, which is important in applications requiring both high-frame rate and constant lateral resolution. Moreover, relative motions of the target with respect to the probe often exist due to hand tremors or biological motions, causing blurring artifacts in the multi-focus image. This article introduces a novel approach for multi-focus US image recovery based on Generative Adversarial Network (GAN) without a reduction in the frame-rate. Herein, a mapping function between the single-focus US image and multi-focus version for having a constant lateral resolution everywhere is estimated through different GANs. We use adversarial loss functions in addition to Mean Square Error (MSE) to generate more realistic ultrasound images. Moreover, we use the boundary seeking method for improving the stability of training, which is currently the main challenge in using GANs. Experiments on simulated and real phantoms as well as on ex vivo data are performed. Results confirm that having both adversarial loss function and boundary seeking training provides better results in terms of the mean opinion score test. Furthermore, the proposed method enhances the resolution and contrast indexes without sacrificing the frame-rate. As for the comparison with other approaches which are not based on NNs, the proposed approach gives similar results while requiring neither channel data nor computationally expensive algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it