Lai Min, Yao Jincao, Zhou Yahan, Zhou Lingyan, Jiang Tian, Sui Lin, Tang Jinglan, Zhu Xinying, Huang Jiaheng, Wang Yifan, Liu Junping, Xu Dong
Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China.
Eur Radiol. 2025 Aug 30. doi: 10.1007/s00330-025-11928-z.
This study aims to explore the feasibility of employing generative adversarial networks (GAN) to generate synthetic contrast-enhanced ultrasound (CEUS) from grayscale ultrasound images of patients with thyroid nodules while dispensing with the need for ultrasound contrast agents injection.
Patients who underwent preoperative thyroid CEUS examinations between January 2020 and July 2022 were collected retrospectively. The cycle-GAN framework integrated paired and unpaired learning modules was employed to develop the non-invasive image generation process. The synthetic CEUS images was generated in three phases: pre-arterial, plateau, and venous. The evaluation included quantitative similarity metrics, classification performance, and qualitative assessment by radiologists.
CEUS videos of 360 thyroid nodules from 314 patients (45 years ± 12 [SD]; 272 women) in the internal dataset and 202 thyroid nodules from 183 patients (46 years ± 13 [SD]; 148 women) in the external dataset were included. In the external testing dataset, quantitative analysis revealed a significant degree of similarity between real and synthetic CEUS images (structure similarity index, 0.89 ± 0.04; peak signal-to-noise ratio, 28.17 ± 2.42). Radiologists deemed 126 of 132 [95%] synthetic CEUS images diagnostically useful. The accuracy of radiologists in distinguishing between real and synthetic images was 55.6% (95% CI: 0.49, 0.63), with an AUC of 61.0% (95% CI: 0.65, 0.68). No statistically significant difference (p > 0.05) was observed when radiologists assessed peak intensity and enhancement patterns using real CEUS and synthetic CEUS.
Both quantitative analysis and radiologist evaluations exhibited that synthetic CEUS images generated by generative adversarial networks were similar to real CEUS images.
QuestionIt is feasible to generate synthetic thyroid contrast-enhanced ultrasound images using generative adversarial networks without ultrasound contrast agents injection. FindingsCompared to real contrast-enhanced ultrasound images, synthetic contrast-enhanced ultrasound images exhibited high similarity and image quality. Clinical relevanceThis non-invasive and intelligent transformation may reduce the requirement for ultrasound contrast agents in certain cases, particularly in scenarios where ultrasound contrast agents administration is contraindicated, such as in patients with allergies, poor tolerance, or limited access to resources.
本研究旨在探讨使用生成对抗网络（GAN）从甲状腺结节患者的灰度超声图像生成合成超声造影（CEUS）的可行性，同时无需注射超声造影剂。
回顾性收集2020年1月至2022年7月期间接受术前甲状腺CEUS检查的患者。采用集成配对和非配对学习模块的循环GAN框架来开发非侵入性图像生成过程。合成CEUS图像分三个阶段生成：动脉前期、平台期和静脉期。评估包括定量相似性指标、分类性能以及放射科医生的定性评估。
纳入内部数据集中314例患者（45岁±12[标准差]；272名女性）的360个甲状腺结节的CEUS视频，以及外部数据集中183例患者（46岁±13[标准差]；148名女性）的202个甲状腺结节的CEUS视频。在外部测试数据集中，定量分析显示真实和合成CEUS图像之间存在显著的相似程度（结构相似性指数，0.89±0.04；峰值信噪比，28.17±2.42）。放射科医生认为132幅[95%]合成CEUS图像中有126幅具有诊断价值。放射科医生区分真实图像和合成图像的准确率为55.6%（95%CI：0.49，0.63），曲线下面积为61.0%（95%CI：0.65，0.68）。当放射科医生使用真实CEUS和合成CEUS评估峰值强度和增强模式时，未观察到统计学显著差异（p>0.05）。
定量分析和放射科医生评估均显示，生成对抗网络生成的合成CEUS图像与真实CEUS图像相似。
问题 无需注射超声造影剂，使用生成对抗网络生成合成甲状腺超声造影图像是可行的。 发现 与真实超声造影图像相比，合成超声造影图像具有高度相似性和图像质量。 临床意义 这种非侵入性和智能转换在某些情况下可能会减少对超声造影剂的需求，特别是在超声造影剂给药禁忌的情况下，如对造影剂过敏、耐受性差或资源有限的患者。
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