Optical coherence tomography angiography (OCTA) shows its great importance in imaging microvascular networks by providing accurate 3D imaging of blood vessels, but it relies upon specialized sensors and expensive devices. For this reason, previous works show the potential to translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images. However, existing OCTA translation methods directly learn the mapping from the OCT domain to the OCTA domain in continuous and infinite space with guidance from only a single view, i.e., the OCTA project map, resulting in suboptimal results. To this end, we propose the multi-view Tri-alignment framework for OCT to OCTA 3D image translation in discrete and finite space, named MuTri. In the first stage, we pre-train two vector-quantized variational auto-encoder (VQ- VAE) by reconstructing 3D OCT and 3D OCTA data, providing semantic prior for subsequent multi-view guidances. In the second stage, our multi-view tri-alignment facilitates another VQVAE model to learn the mapping from the OCT domain to the OCTA domain in discrete and finite space. Specifically, a contrastive-inspired semantic alignment is proposed to maximize the mutual information with the pre-trained models from OCT and OCTA views, to facilitate codebook learning. Meanwhile, a vessel structure alignment is proposed to minimize the structure discrepancy with the pre-trained models from the OCTA project map view, benefiting from learning the detailed vessel structure information. We also collect the first large-scale dataset, namely, OCTA2024, which contains a pair of OCT and OCTA volumes from 846 subjects.

翻译：光学相干断层扫描血管成像（OCTA）通过提供精确的血管三维成像，在微血管网络成像中展现出其重要性，但其依赖于专用传感器和昂贵设备。因此，先前的研究展示了将易于获取的三维光学相干断层扫描（OCT）图像转换为三维OCTA图像的潜力。然而，现有的OCTA转换方法仅在单一视角（即OCTA投影图）的引导下，于连续无限空间中直接学习从OCT域到OCTA域的映射，导致结果欠佳。为此，我们提出了一种在离散有限空间中实现OCT至OCTA三维图像转换的多视角三重对齐框架，命名为MuTri。在第一阶段，我们通过重建三维OCT和三维OCTA数据预训练两个向量量化变分自编码器（VQ-VAE），为后续的多视角引导提供语义先验。在第二阶段，我们的多视角三重对齐促使另一个VQVAE模型在离散有限空间中学习从OCT域到OCTA域的映射。具体而言，我们提出了一种受对比学习启发的语义对齐方法，以最大化与来自OCT和OCTA视角的预训练模型之间的互信息，从而促进码本学习。同时，我们提出了一种血管结构对齐方法，以最小化与来自OCTA投影图视角的预训练模型之间的结构差异，这有助于学习详细的血管结构信息。我们还收集了首个大规模数据集OCTA2024，该数据集包含来自846名受试者的成对OCT和OCTA三维体数据。