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通过生成对抗网络合成甲状腺结节的对比增强超声波图像

Min Lai^1,2,3,4, Jincao Yao², Yahan Zhou^2,3,5

¹Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.

European radiology

|August 30, 2025

概括

生成对抗网络 (GAN) 可以在没有对比剂的情况下为甲状腺结节创建合成对比增强超声波 (CEUS) 图像. 这些人工智能生成的图像与真实CEUS具有很高的相似性,在临床环境中提供了潜在的替代方案.

科学领域:

医学成像
人工智能
超声波技术

背景情况:

对比度增强超声波 (CEUS) 对于甲状腺结节的评估至关重要.
超声波对比剂 (UCAs) 对于CEUS至关重要,但具有禁忌和限制.
开发用于CEUS图像生成的非侵入性方法是非常理想的.

研究的目的:

评估使用生成对抗网络 (GAN) 来生成甲状腺结节的合成 CEUS 图像的可行性.
在CEUS程序中消除了对比剂注射的需要.
评估GAN生成的合成CEUS图像的质量和诊断效用.

主要方法:

甲状腺CEUS检查的回顾性收集
使用了一个循环GAN框架,配对和不配对的学习模块.
在动脉前,平原和静脉阶段生成合成CEUS图像.
使用定量指标,分类性能和放射学家评估评估的图像.

主要成果:

外部数据集中的真实和合成CEUS图像具有很高的相似性 (SSIM: 0.89,PSNR: 28.17).
放射科医生发现95%的合成CEUS图像在诊断上有用.
在真实和合成CEUS之间,峰值强度和增强模式没有显著差异.

结论:

生成对抗网络 (GAN) 可以成功生成甲状腺结节的合成 CEUS 图像.
与真实CEUS相比,生成的合成CEUS图像具有很高的相似性和诊断价值.
这种非侵入性方法可以减少对超声波对比剂的依赖,扩大CEUS的适用性.

关键词:

人工智能对比介质产生性对抗性网络甲状腺结节超声波检查

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