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Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing an improved generative adversarial model (GAN) to synthesize virtual EUS (V-EUS) from conventional B-mode images. Specifically, a bi-discriminator structure and a color prior module are designed to model the intrinsic attributes of the EUS. A total of 4580 cases were collected from 15 medical centers and extensive experiments were designed to demonstrate the validity of the proposed model. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5 \(\%\) after V-EUS is equipped.

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Virtual Elastography Ultrasound via Generative Adversarial Network and Its Application to Breast Cancer Diagnosis

  • 章节

  • 发表日期：

    2025年

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  • Zhao Yao,
  • Yuanyuan Wang,
  • Min Liu,
  • Jianqiao Zhou,
  • Jinhua Yu

摘要

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing an improved generative adversarial model (GAN) to synthesize virtual EUS (V-EUS) from conventional B-mode images. Specifically, a bi-discriminator structure and a color prior module are designed to model the intrinsic attributes of the EUS. A total of 4580 cases were collected from 15 medical centers and extensive experiments were designed to demonstrate the validity of the proposed model. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5 \(\%\) after V-EUS is equipped.