TY - JOUR
T1 - Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming
AU - Zhang, Zhiming
AU - Lei, Zhenyu
AU - Zhou, Mengchu
AU - Hasegawa, Hideyuki
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2025
Y1 - 2025
N2 - Ultrasound detection is a potent tool for the clinical diagnosis of various diseases due to its real-time, convenient, and noninvasive qualities. Yet, existing ultrasound beamforming and related methods face a big challenge to improve both the quality and speed of imaging for the required clinical applications. The most notable characteristic of ultrasound signal data is its spatial and temporal features. Because most signals are complex-valued, directly processing them by using real-valued networks leads to phase distortion and inaccurate output. In this study, for the first time, we propose a complex-valued convolutional gated recurrent (CCGR) neural network to handle ultrasound analytic signals with the aforementioned properties. The complex-valued network operations proposed in this study improve the beamforming accuracy of complex-valued ultrasound signals over traditional real-valued methods. Further, the proposed deep integration of convolution and recurrent neural networks makes a great contribution to extracting rich and informative ultrasound signal features. Our experimental results reveal its outstanding imaging quality over existing state-of-the-art methods. More significantly, its ultrafast processing speed of only 0.07 s per image promises considerable clinical application potential. The code is available at https://github.com/zhangzm0128/CCGR.
AB - Ultrasound detection is a potent tool for the clinical diagnosis of various diseases due to its real-time, convenient, and noninvasive qualities. Yet, existing ultrasound beamforming and related methods face a big challenge to improve both the quality and speed of imaging for the required clinical applications. The most notable characteristic of ultrasound signal data is its spatial and temporal features. Because most signals are complex-valued, directly processing them by using real-valued networks leads to phase distortion and inaccurate output. In this study, for the first time, we propose a complex-valued convolutional gated recurrent (CCGR) neural network to handle ultrasound analytic signals with the aforementioned properties. The complex-valued network operations proposed in this study improve the beamforming accuracy of complex-valued ultrasound signals over traditional real-valued methods. Further, the proposed deep integration of convolution and recurrent neural networks makes a great contribution to extracting rich and informative ultrasound signal features. Our experimental results reveal its outstanding imaging quality over existing state-of-the-art methods. More significantly, its ultrafast processing speed of only 0.07 s per image promises considerable clinical application potential. The code is available at https://github.com/zhangzm0128/CCGR.
KW - Complex-valued neural network
KW - convolutional neural network
KW - deep learning
KW - gated recurrent unit (GRU)
KW - ultrasound beamforming
UR - http://www.scopus.com/inward/record.url?scp=86000430385&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2024.3384314
DO - 10.1109/TNNLS.2024.3384314
M3 - 学術論文
C2 - 38598398
AN - SCOPUS:86000430385
SN - 2162-237X
VL - 36
SP - 5668
EP - 5679
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 3
ER -