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 |
|---|---|
| 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 | 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 |
Conference
| Conference | 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 |
|---|---|
| Country | United Kingdom |
| City | Brighton |
| Period | 12/05/19 → 17/05/19 |
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Keywords
- Adaptive Beamforming
- Deep Learning
- Plane Wave Imaging
- Ultrasound
Cite this
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Deep learning for fast adaptive beamforming. / Luijten, Ben; Cohen, Regev; de Bruijn, Frederik J.; Schmeitz, Harold A.W.; Mischi, Massimo; Eldar, Yonina C.; van Sloun, Ruud J.G.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 1333-1337 8683478.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Deep learning for fast adaptive beamforming
AU - Luijten, Ben
AU - Cohen, Regev
AU - de Bruijn, Frederik J.
AU - Schmeitz, Harold A.W.
AU - Mischi, Massimo
AU - Eldar, Yonina C.
AU - van Sloun, Ruud J.G.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
KW - Adaptive Beamforming
KW - Deep Learning
KW - Plane Wave Imaging
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85068999220&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8683478
DO - 10.1109/ICASSP.2019.8683478
M3 - Conference contribution
AN - SCOPUS:85068999220
SP - 1333
EP - 1337
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -