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PURPOSE Optical coherence tomography angiography (OCTA) is a premium imaging modality for non-invasive 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: 1) the angiogram information extraction is only limited to the locally consecutive B-scans; 2) 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. METHODS 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. RESULTS 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. CONCLUSIONS 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. This article is protected by copyright. All rights reserved.