对抗式学习用于超声医学成像中的束形域转移
Silvia Seoni1, Massimo Salvi1, Giulia Matrone2
1Biolab, PoliTo(BIO)Med Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
Ultrasonics
|July 17, 2025
概括
生成对抗网络 (GAN) 可以通过将标准的延迟和和 (DAS) 图像转换为更高质量的过延迟乘法和和 (F-DMAS) 图像来增强超声波图像,即使没有原始射频数据.
科学领域:
- 医学成像医学成像
- 超声波技术的超声波技术
- 医疗保健中的人工智能
背景情况:
- 超声波图像质量严重依赖于光束成形技术.
- 像过延迟乘法和总和 (F-DMAS) 等先进的光束成形方法提供了比传统的延迟和总和 (DAS) 优越的图像质量.
- 在临床超声波扫描仪中,对原始射频 (RF) 数据的访问往往受到限制,限制了先进光束成形的使用.
研究的目的:
- 引入和验证一种生成对抗网络 (GAN) 方法来增强B模式超声波图像.
- 为了将传统的平面波DAS图像转换为类似于F-DMAS产生的图像,而不需要原始射频数据.
- 为了比较不同GAN架构 (Pix2Pix,Pyramidal Pix2Pix,CycleGAN) 与传统增强方法的性能.
主要方法:
- 采用了三个GAN架构:传统的Pix2Pix,Pyramidal Pix2Pix和CycleGAN,从DAS图像中生成类似F-DMAS的图像.
- 使用全参考指标 (根平均平方误差 - RMSE,峰值信号与噪声比 - PSNR) 和纹理分析 (27个参数) 验证的图像质量.
- 通过专家的定性评估评估对比度增强 (对比度改善指数 - CII) 和临床相关性,与CLAHE和组图匹配进行比较.
主要成果:
- 金字塔Pix2Pix GAN实现了最佳的图像相似度指标 (PSNR = 18.0 ± 0.6 dB,RMSE = 0.126 ± 0.008),超过了传统方法.
- 纹理分析证实,生成模型产生的图像更接近目标F-DMAS,与传统增强技术区分开来.
- 所有方法都改善了对比度;专家评估确定了具有临床相关性的最优质GAN产生的图像.
结论:
- 生成对抗网络提供了一个可行的解决方案,用于增强B模式超声波图像,当原始射频数据是不可访问的.
- 纹理特征作为有效的歧视者,用于验证医学成像中的深度学习生成模型.
- 提出的基于GAN的方法成功模拟了F-DMAS图像质量,改善了没有原始数据的诊断潜力.
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