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基于对抗神经网络中的频率和空间信息的OCT图像的超分辨率重建
Wei Xia1,2, Tingting Han1, Kuiyuan Tao3
1Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China.
Physics in medicine and biology
|November 5, 2025
概括
这项研究引入了一种先进的神经网络,用于增强光学连贯性断层扫描 (OCT) 图像. 该方法提高了图像分辨率和细节,有助于疾病诊断和治疗.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 光学连贯断层扫描 (OCT) 对于诊断心脏和眼部疾病至关重要.
- 图像质量的局限性,包括硬件限制,低采样率和噪音,阻碍了分辨率和细节可见性.
- 为了准确的临床评估,需要提高OCT图像分辨率.
研究的目的:
- 开发一个改进的超分辨率 (SR) 重建方法用于OCT图像.
- 为了解决当前的OCT成像硬件和处理技术的局限性.
- 为了提高OCT扫描中的细节和整体图像质量的可见性.
主要方法:
- 提出了一个对抗性神经网络,将空间和频率信息集成为OCT SR重建.
- 开发了专门的模块 (FCBR和PixelShuffler-FCBR) 用于特征提取和融合.
- 用于频域处理的使用的频率-卷积-批量规范-修正线性单位 (FCBR).
主要成果:
- 在冠状动脉和眼科数据集的OCT图像细节恢复方面取得了实质性的改进.
- 与现有的最先进的方法相比,实现了优越的频域忠实性.
- 在重建的OCT图像中展示了增强的整体视觉质量和纹理恢复.
结论:
- 拟议的方法有效地利用全球频率和本地空间特征,用于优质的OCT图像重建.
- 显著改善了OCT图像的纹理恢复和结构一致性.
- 有潜力提高受OCT成像分辨率限制影响的疾病的临床定量评估.
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