Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming

Zhiming Zhang, Zhenyu Lei, Meng Chu Zhou, Hideyuki Hasegawa, Shangce Gao

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
StateAccepted/In press - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


  • Complex-valued neural network
  • convolutional neural network
  • deep learning
  • gated recurrent unit (GRU)
  • ultrasound beamforming


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