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 euristic ontextual uidance 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 essel romoted uidance 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: .

中文翻译：

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通过启发式上下文约束将容器提升的 OCT 转换为 OCTA 图像


光学相干断层扫描血管造影 (OCTA) 是视网膜疾病临床筛查的重要工具，可通过非侵入性扫描对血管进行精确的 3D 成像。然而，由于需要专门的传感器和昂贵的设备，基于硬件的 OCTA 图像获取方法面临着挑战。在本文中，我们介绍了一种称为 TransPro 的新颖方法，该方法可以将现成的 3D 光学相干断层扫描 (OCT) 图像转换为 3D OCTA 图像，而无需任何额外的硬件修改。我们的 TransPro 方法主要由两个新颖的想法驱动，而这两个想法被之前的工作所忽视。第一个想法源自一项重要的观察，即 OCTA 投影图是通过对沿 Z 轴的相应 B 扫描的像素值进行平均而生成的。因此，我们引入了一种混合架构，结合了 3D 对抗生成网络和新颖的启发式文本指导模块，它有效地保持了 3D 体积和投影图之间生成的 OCTA 图像的一致性。第二个想法是提高翻译后的 OCTA 投影图中的船只质量。因此，我们提出了一种新颖的 essel 远程导航模块来增强网络对视网膜血管的关注。两个数据集上的实验结果表明，我们的 TransPro 优于最先进的方法，与相比，MAE 相对提高了 11.4%，PSNR 提高了 2.7%，SSIM 提高了 2%，VDE 提高了 40%，VDC 提高了 9.1%基线方法。该代码可在以下位置获取： 。