Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.

翻译：光学相干断层扫描血管成像（OCTA）是视网膜疾病临床筛查的关键工具，可通过无创扫描实现血管的精确三维成像。然而，基于硬件的OCTA图像获取方法因需要专用传感器和昂贵设备而面临挑战。本文提出了一种名为TransPro的新方法，能够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，且无需任何额外的硬件修改。我们的TransPro方法主要基于两个先前研究忽略的新颖思想。第一个思想源于一个重要观察：OCTA投影图是通过沿Z轴平均其对应B扫描的像素值生成的。因此，我们引入了一种混合架构，结合了三维对抗生成网络和一个新颖的启发式上下文引导（HCG）模块，该模块有效保持了生成OCTA图像在三维体数据与投影图之间的一致性。第二个思想是提升转换后OCTA投影图中的血管质量。为此，我们提出了一种新颖的血管增强引导（VPG）模块，以增强网络对视网膜血管的关注度。在两个数据集上的实验结果表明，我们的TransPro方法优于现有先进方法，与基线方法相比，在MAE上相对提升约11.4%，PSNR提升2.7%，SSIM提升2%，VDE提升40%，VDC提升9.1%。代码发布于：https://github.com/ustlsh/TransPro。