Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China.
Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Med Phys. 2022 Jun;49(6):3705-3716. doi: 10.1002/mp.15618. Epub 2022 Mar 28.
Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: (a) the angiogram information extraction is only limited to the locally consecutive B-scans; and (b) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood information-fused Pseudo-3D U-Net (NI-P3D-U) for OCTA reconstruction.
The proposed NI-P3D-U was investigated on an in vivo animal dataset by a cross-validation strategy under both fully supervised learning and weakly supervised learning pipelines. To demonstrate the OCTA reconstruction capability of the proposed NI-P3D-U, we compared it with several state-of-the-art methods.
The results showed that the proposed network outperformed the state-of-the-art deep learning-based OCTA algorithms in terms of visual quality and quantitative metrics, and demonstrated an effective generalization for different training strategies (fully supervised and weakly supervised) and imaging protocols. Meanwhile, the idea of neighborhood information fusion was also expanded to other network architectures, resulting in significant improvements.
These investigations indicate that the proposed network, which combines the neighborhood information strategy with temporal modeling architecture, is well capable of performing OCTA reconstruction, and has a certain potential for clinical applications.
光学相干断层扫描血管造影术（OCTA）是一种非侵入性微血管研究的高级成像方式。深度学习网络在 OCTA 重建任务中取得了有希望的结果，受益于其强大的建模能力。然而，当前基于深度学习的 OCTA 重建方法存在两个局限性：（a）血管造影信息提取仅限于局部连续的 B 扫描；（b）所有重建模型都局限于 2D 卷积网络架构，缺乏有效的时间建模。因此，OCTA 的有价值的邻域信息和固有时间特征没有得到充分利用。在本文中，我们设计了一种用于 OCTA 重建的邻域信息融合伪 3D U-Net（NI-P3D-U）。
通过交叉验证策略，在体内动物数据集上对所提出的 NI-P3D-U 进行了研究，包括完全监督学习和弱监督学习两种管道。为了展示所提出的 NI-P3D-U 的 OCTA 重建能力，我们将其与几种最先进的方法进行了比较。
结果表明，在所提出的网络在视觉质量和定量指标方面优于最先进的基于深度学习的 OCTA 算法，并证明了对不同训练策略（完全监督和弱监督）和成像协议的有效泛化能力。同时，邻域信息融合的思想也扩展到了其他网络架构，取得了显著的改进。
这些研究表明，所提出的网络将邻域信息策略与时间建模架构相结合，能够很好地进行 OCTA 重建，并且具有一定的临床应用潜力。
