ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020
Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- From image to insight: an AI-enabled framework for echocardiography acquisition, reconstruction, interpretation and interaction.European heart journal. Digital health · 2026Review
- Training Deep Network Ultrasound Beamformers With Unlabeled In Vivo Data.IEEE transactions on medical imaging · 2022Article
- Advances in ultrasonography: image formation and quality assessment.Journal of medical ultrasonics (2001) · 2021Review
- Evaluating Input Domain and Model Selection for Deep Network Ultrasound Beamforming.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We study training deep neural network (DNN) frequency-domain beamformers using simulated and phantom anechoic cysts and compare to training with simulated point target responses. Using simulation, physical phantom, and in vivo scans, we find that training DNN beamformers using anechoic cysts provided comparable or improved image quality compared with training DNN beamformers using simulated point targets. The proposed method could also be adapted to generate training data from in vivo scans. Finally, we evaluated the robustness of DNN beamforming to common sources of image degradation, including gross sound speed errors, phase aberration, and reverberation. We found that DNN beamformers maintained their ability to improve image quality even in the presence of the studied sources of image degradation. Overall, the results show the potential of using DNN beamforming to improve ultrasound image quality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.