在光学连贯断层扫描中使用相位感知生成对抗网络进行复杂的结合物去除
Valentina Bellemo1,2,3, Richard Haindl4, Manojit Pramanik5
1Nanyang Technological University, School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore.
Journal of biomedical optics
|February 18, 2025
概括
一种新的深度学习方法使用生成对抗网络从光学连贯断层扫描 (OCT) 扫描中删除复杂的结合元件 (CCA). 这种基于软件的方法消除了对额外硬件的需求,为增强成像提供了具有成本效益的解决方案.
科学领域:
- 生物医学光学 生物医学光学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在频域光连贯断层扫描 (FD-OCT) 中,复杂的并联器件 (CCA) 需要额外的硬件,从而增加了系统的复杂性和成本.
- 基于软件的CCA移除解决方案对于效率和成本效益来说是非常理想的.
研究的目的:
- 开发一种深度学习方法,以便在OCT扫描中有效地去除CCA.
- 在FD-OCT系统中消除了对额外硬件组件的需求.
主要方法:
- 实施使用生成对抗网络 (GAN) 的深度学习方法.
- 利用来自OCT扫描的强度和相位图像来改善文物移除.
- 开发一个CCA去除-GAN模型.
主要成果:
- 成功地将OCT扫描与CCA转换为各种样本 (幽灵,人类皮肤,老鼠眼睛) 的无文物扫描.
- 使用相位稳定扫描源OCT原型进行体内成像的演示.
- 通过相位图像的集成,在CCA去除方面显著提高了性能.
结论:
- 开发的方法提供了一个低成本的,数据驱动的,基于软件的CCA去除解决方案.
- 通过有效的工件减少,增强FD-OCT成像能力.
- 为基于硬件的CCA移除技术提供了可行的替代方案.
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