Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming

Zhiming Zhang, Zhenyu Lei*, Mengchu Zhou, Hideyuki Hasegawa, Shangce Gao*

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

5 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)5668-5679
ページ数12
ジャーナルIEEE Transactions on Neural Networks and Learning Systems
36
3
DOI
出版ステータス出版済み - 2025

ASJC Scopus 主題領域

  • ソフトウェア
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 人工知能

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