Badawi Nadia, Rustamov Jaloliddin, Rustamov Zahiriddin, Lesage Frederic, Zaki Nazar, Damseh Rafat
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, UAE.
Biomedical Engineering Institute, Ecole Polytechnique de Montreal, Montreal, QC, Canada.
Sci Rep. 2025 Jul 12;15(1):25182. doi: 10.1038/s41598-025-07410-x.
Modeling microscopic cerebrovascular networks is essential for understanding cerebral blood flow and oxygen transport. High-resolution imaging modalities, such as Optical Coherence Tomography (OCT) and Two-Photon Microscopy (TPM), are widely used to capture microvascular structure and topology. Although TPM angiography generally provides better localization and image quality than OCT, its use is impractical in studies involving fluorescent dye leakage. Here, we exploit generative adversarial learning to produce high-quality TPM angiographies from OCT vascular stacks. We investigate the use of 2D and 3D cycle generative adversarial networks (CycleGANs) trained on unpaired image samples. We evaluate the generated TPM vascular structures based on image similarity and signal-to-noise ratio. Additionally, we evaluated the generated vascular structures after applying vessel segmentation and extracting their 3D topological models. Our results demonstrate that the 2D adversarial learning model outperforms the 3D model in terms of image quality. However, our statistical comparisons of vascular network features show the 3D model's consistent superiority in generating vascular structures. Our work provides a complementary approach to enhance vascular analysis when only OCT imaging is available.
对微观脑血管网络进行建模对于理解脑血流和氧运输至关重要。高分辨率成像方式，如光学相干断层扫描（OCT）和双光子显微镜（TPM），被广泛用于获取微血管结构和拓扑。尽管TPM血管造影术通常比OCT提供更好的定位和图像质量，但在涉及荧光染料泄漏的研究中其使用并不实际。在此，我们利用生成对抗学习从OCT血管堆栈生成高质量的TPM血管造影。我们研究在未配对图像样本上训练的二维和三维循环生成对抗网络（CycleGAN）的使用。我们基于图像相似性和信噪比评估生成的TPM血管结构。此外，我们在应用血管分割并提取其三维拓扑模型后评估生成的血管结构。我们的结果表明，二维对抗学习模型在图像质量方面优于三维模型。然而，我们对血管网络特征的统计比较表明，三维模型在生成血管结构方面具有持续的优势。我们的工作提供了一种补充方法，以便在仅可获得OCT成像时增强血管分析。
