基于深度学习的光学连贯性断层扫描重建用于高速和对比形态和血管成像
Yudan Chen1, Shuo Chen2, Jun Song1
1University of British Columbia, School of Biomedical Engineering, Faculty of Medicine and Applied Science, Vancouver, British Columbia, Canada.
Journal of biomedical optics
|February 6, 2026
概括
这项研究引入了一个深度学习模型,以提高光谱域光学连贯性断层扫描 (SD-OCT) 成像速度和灵敏度. 这种方法可以提高图像质量和视网膜层可视化,而无需进行硬件更改.
科学领域:
- 眼科成像 眼科成像
- 医疗图像分析 医学图像分析
- 深度学习应用程序深度学习应用程序
背景情况:
- 光谱域光学连贯性断层扫描 (SD-OCT) 提供了高分辨率,但在成像速度和灵敏度方面面临挑战.
- 在SD-OCT中同时提高速度和灵敏度是一个重要的技术障碍.
研究的目的:
- 开发一种深度学习方法,以提高SD-OCT系统的成像速度和灵敏度.
- 通过先进的计算方法克服SD-OCT中的硬件限制.
主要方法:
- 使用了修改后的U-Net深度神经网络 (DNN) 架构,该架构结合了视觉状态空间模型.
- 该模型从高速采集中合成了高信号噪声比 (SNR) 的OCT和OCT血管学 (OCTA).
- 使用诸如多尺度结构相似度指数 (MS-SSIM) 和对比度与噪声比 (CNR) 等量化指标来评估性能.
主要成果:
- 深度学习模型有效地重建了高SNR的OCT和OCTA图像.
- 观察到视网膜层之间的对比度得到改善,并加强了层边界的划分.
- 精细的视网膜结构,包括微毛细管和胆管,已成功恢复.
结论:
- 一个基于DNN的新架构可以同时提高SD-OCT成像速度和灵敏度.
- 这种方法可以提高OCT的图像质量,而不会影响获取速度.
- 该方法为克服SD-OCT系统固有的硬件限制提供了可行的解决方案.
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