跨域未配对学习用于低剂量CT成像
IEEE journal of biomedical and health informatics
|September 7, 2023
概括
本研究介绍了CrossDuL,这是一个用于生成伪低剂量计算机断层扫描 (LDCT) 数据的新框架. 这种方法可以使用监督深度学习来改进LDCT图像重建,即使是未配对的数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 监督深度学习在低剂量计算机断层扫描 (LDCT) 成像中表现出色,但需要配对数据,这在临床上很难获得.
- 缺少配对数据阻碍了监督深度学习用于LDCT图像增强的广泛临床应用.
- 从未配对的数据集中生成现实的伪配对LDCT数据是具有挑战性的,因为噪声的复杂性质.
研究的目的:
- 开发一个有效的跨领域的未配对学习框架,用于生成伪LDCT数据.
- 为了使LDCT图像重建使用易于获得的未配对数据进行监督深度学习.
- 提高LDCT成像在临床实践中的性能.
主要方法:
- 提出了一个名为CrossDuL的跨领域未配对学习框架.
- 开发了一种专门的伪LDCT协同图生成模块,使用数据依赖的噪声模型.
- 在图像域 (而不是sinogram域) 中构建了一个伪配对数据集,以训练一个LDCT图像恢复模块.
主要成果:
- 该CrossDuL框架成功生成了伪LDCT数据.
- 对伪配对图像数据集的训练导致了有效的LDCT图像重建.
- 临床数据集在LDCT成像性能方面显示出有前途的定量和质量改进.
结论:
- 对于LDCT来说,CrossDuL框架提供了一个可行的解决方案,以克服对联数据限制在监督深度学习中.
- 拟议的方法有助于生成伪配对数据和随后的图像重建.
- CrossDuL显示了提高临床LDCT成像质量和采用的巨大潜力.
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