Abstract
The real-time nature that makes diagnostic ultrasonography so appealing to clinicians imposes strong constraints on the computational complexity of image reconstruction algorithms. As such, these typically rely on traditional delay-and-sum beamforming, a low-complexity approach that unfortunately comes at the cost of reduced image quality as compared to more advanced and content-adaptive beamformers. Here, we propose a model-aware deep learning strategy to ultrasound image reconstruction, which leverages knowledge of minimum variance beamforming while exploiting the efficiency of deep neural networks. Our approach yields high quality images with strong contrast at real-time reconstruction rates. The neural network is trained using in vivo and simulated radio frequency channel data of a single plane wave transmit, and corresponding high-quality minimum-variance beamformed reconstructions. Performance is benchmarked using simulated acquisitions from the PICMUS [1] dataset, demonstrating the convincing generalizability and image quality of the proposed beamformer.
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1333-1337 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-8131-1 |
DOIs | |
Publication status | Published - 1 May 2019 |
Event | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) - Brighton Conference Centre, Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 https://2019.ieeeicassp.org/ |
Conference
Conference | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) |
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Abbreviated title | ICASSP 2019 |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/19 → 17/05/19 |
Internet address |
Keywords
- Adaptive Beamforming
- Deep Learning
- Plane Wave Imaging
- Ultrasound