Guo Zezhao, Zhao Zhanfang
College of Information and Engineering, Hebei GEO University, Hebei, China.
Sci Rep. 2025 Mar 3;15(1):7405. doi: 10.1038/s41598-024-82812-x.
Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology that provides cross-sectional images of tissues. Dense acquisition of A-scans along the fast axis is required to obtain high digital resolution images. However, the dense acquisition will increase the acquisition time, causing the discomfort of patients. In addition, the longer acquisition time may lead to motion artifacts, thereby reducing imaging quality. In this work, we proposed a hybrid attention structure preserving network (HASPN) to achieve super-resolution of under-sampled OCT images to speed up the acquisition. It utilized adaptive dilated convolution-based channel attention (ADCCA) and enhanced spatial attention (ESA) to better capture the channel and spatial information of the feature. Moreover, convolutional neural networks (CNNs) exhibit a higher sensitivity of low-frequency than high-frequency information, which may lead to a limited performance on reconstructing fine structures. To address this problem, we introduced an additional branch, i.e., textures & details branch, using high-frequency decomposition images to better super-resolve retinal structures. The superiority of our method was demonstrated by qualitative and quantitative comparisons with mainstream methods. Furthermore, HASPN was applied to three out-of-distribution datasets, validating its strong generalization capability.
光学相干断层扫描（OCT）是一种非侵入性的高分辨率成像技术，可提供组织的横截面图像。为了获得高数字分辨率图像，需要沿快轴密集采集A扫描。然而，密集采集会增加采集时间，给患者带来不适。此外，较长的采集时间可能会导致运动伪影，从而降低成像质量。在这项工作中，我们提出了一种混合注意力结构保留网络（HASPN），以实现欠采样OCT图像的超分辨率，从而加快采集速度。它利用基于自适应扩张卷积的通道注意力（ADCCA）和增强空间注意力（ESA）来更好地捕捉特征的通道和空间信息。此外，卷积神经网络（CNN）对低频信息的敏感度高于高频信息，这可能导致在重建精细结构时性能有限。为了解决这个问题，我们引入了一个额外的分支，即纹理和细节分支，使用高频分解图像来更好地超分辨率视网膜结构。通过与主流方法的定性和定量比较，证明了我们方法的优越性。此外，HASPN被应用于三个分布外数据集，验证了其强大的泛化能力。
