CCGR

Paper | Project Page

CCGR: Complex-valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming

Zhiming Zhang, Zhenyu Lei, MengChu Zhou, Hideyuki Hasegawa, Shangce Gao

CCGR is a Complex-valued Convolutional Gated Recurrent neural network for beamforming complex-valued ultrasound analytic signals with spatio-temporal features. Our experimental results reveal its outstanding imaging quality over existing state-of-the-art methods. More significantly, its ultrafast processing speed of only 0.07s per image promises its considerable clinical application potential.

Key Features

• Complex-Valued Operations: CCGR utilizes complex-valued convolutional gates to process ultrasound analytic signals effectively.
• Enhanced Accuracy: Compared to traditional methods, CCGR offers superior beamforming accuracy, ensuring high-quality imaging results.
• Deep Integration: The seamless integration of convolution and recurrent neural network architectures enhances feature extraction and signal processing capabilities.
• Ultrafast Processing: With an impressive processing speed of only 0.07s per image, CCGR holds promise for rapid clinical applications.

Getting Started

Training

Train the DVT on Nvidia GPU.

python main.py --mode train --device cuda --config ./config.json

Prediction

Test a model on Nvidia GPU.

python main.py --mode predict --device cuda --checkpoint ./logs/xxx

Citing CCGR

Zhiming Zhang, Zhenyu Lei, MengChu Zhou, Hideyuki Hasegawa, and Shangce Gao, “Complex-valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming,” IEEE Transactions on Neural Networks and Learning Systems, 2024. DOI: 10.1109/TNNLS.2024.3384314.

@article{zhang2024complex,
  author={Zhiming Zhang,Zhenyu Lei,MengChu Zhou,Hideyuki Hasegawa,Shangce Gao},
  title={Complex-valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming},
  journal={IEEE Transactions on Neural Networks and Learning Systems},  
  year={2024},
  doi={10.1109/TNNLS.2024.3384314}
}