用深度学习实现OCT与OCTA图像双向转换，无需昂贵设备即可获得血管信息。
PupiNet: Seamless OCT-OCTA Interconversion Through Wavelet-Driven and Multi-Scale Attention Mechanisms
- 结合小波变换与多尺度注意力机制，提升图像转换质量。
- 在300例眼病数据上验证，生成图像血管结构与真实图像高度一致。
- 适合眼科临床、设备受限场景下快速获取OCTA信息的研究者使用。
光学相干断层扫描（OCT）和光学相干断层扫描血管成像（OCTA）是视网膜疾病诊断的重要工具。相比传统OCT，OCTA可提供更丰富的微血管信息，但其获取需专用传感器和高成本设备，限制了临床部署。针对OCTA采集复杂性和潜在机械伪影问题，本文提出PupiNet——一种双向图像转换框架，可准确实现三维OCT与三维OCTA之间的相互转换。该框架的生成器模块创新融合小波变换与多尺度注意力机制，显著提升转换质量；判别器引入自适应判别器增强（ADA）模块，优化训练稳定性和收敛效率。为保障生成图像中血管结构的临床准确性，设计了血管结构匹配（VSM）监督模块，实现生成图像与目标图像间血管形态的精准匹配；层级特征校准（HFC）模块则确保不同深度层级纹理细节的高度一致性。在包含300例具有多种视网膜病变的眼部配对OCT-OCTA数据集上进行了全面评估，实验结果表明，PupiNet不仅能可靠实现两模态间的高质量双向转换，且在图像保真度、血管结构保留和临床可用性方面均表现优异。
原文摘要 · Abstract (English)
Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) are key diagnostic tools for clinical evaluation and management of retinal diseases. Compared to traditional OCT, OCTA provides richer microvascular information, but its acquisition requires specialized sensors and high-cost equipment, creating significant challenges for the clinical deployment of hardware-dependent OCTA imaging methods. Given the technical complexity of OCTA image acquisition and potential mechanical artifacts, this study proposes a bidirectional image conversion framework called PupiNet, which accurately achieves bidirectional transformation between 3D OCT and 3D OCTA. The generator module of this framework innovatively integrates wavelet transformation and multi-scale attention mechanisms, significantly enhancing image conversion quality. Meanwhile, an Adaptive Discriminator Augmentation (ADA) module has been incorporated into the discriminator to optimize model training stability and convergence efficiency. To ensure clinical accuracy of vascular structures in the converted images, we designed a Vessel Structure Matcher (VSM) supervision module, achieving precise matching of vascular morphology between generated images and target images. Additionally, the Hierarchical Feature Calibration (HFC) module further guarantees high consistency of texture details between generated images and target images across different depth levels. To rigorously validate the clinical effectiveness of the proposed method, we conducted a comprehensive evaluation on a paired OCT-OCTA image dataset containing 300 eyes with various retinal pathologies. Experimental results demonstrate that PupiNet not only reliably achieves high-quality bidirectional transformation between the two modalities but also shows significant advantages in image fidelity, vessel structure preservation, and clinical usability.
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