Freqformer：用于人类视网膜脉管系统三维重建与定量分析的频域 Transformer
Freqformer: Frequency-Domain Transformer for 3-D Reconstruction and Quantification of Human Retinal Vasculature
AI总结：
本文提出频域 Transformer 模型 Freqformer，通过双分支空间与复值频域建模，从单次 OCTA 扫描可靠重建三维视网膜微血管并支持精确定量。
摘要：
目的：从单次光学相干断层扫描血管造影（OCTA）扫描中实现人类视网膜脉管系统的准确三维重建与定量分析。方法：我们提出 Freqformer，这是一种新型的基于 Transformer 的模型，具有双分支架构，该架构集成了用于捕获全局空间上下文的 Transformer 层，以及专为自适应频率增强设计的复值频域模块。Freqformer 使用单深度平面 OCTA 图像进行训练，并采用体积合并 OCTA 作为真值。性能通过二维和三维图像质量指标进行定量评估。研究比较了二维网络及其三维对应模型，以评估逐切片增强体积与通过三维块增强体积之间的差异。此外，还进行了三维定量血管指标分析，以量化人类视网膜脉管系统。结果：Freqformer 显著优于现有卷积神经网络和基于 Transformer 的方法，取得了更优的图像指标。重要的是，增强后的 OCTA 体积在血管段数量、密度、长度和血流指数方面与合并体积表现出强相关性，进一步强调了其用于定量血管分析的可靠性。三维对应模型并未在图像指标或下游三维血管定量方面产生额外增益，却导致推理时间延长近一个数量级，这支持了我们的二维逐切片增强策略。此外，Freqformer 在更大视场扫描上表现出优异的泛化能力，其质量超过传统体积合并方法。结论：Freqformer 能够从单次扫描 OCTA 可靠地生成高清三维视网膜微血管，实现可与标准体积合并方法相媲美的精确血管定量。
英文摘要：
Objective: To achieve accurate 3-D reconstruction and quantitative analysis of human retinal vasculature from a single optical coherence tomography angiography (OCTA) scan. Methods: We introduce Freqformer, a novel Transformer-based model featuring a dual-branch architecture that integrates a Transformer layer for capturing global spatial context with a complex-valued frequency-domain module designed for adaptive frequency enhancement. Freqformer was trained using single depth-plane OCTA images, utilizing volumetrically merged OCTA as the ground truth. Performance was evaluated quantitatively through 2-D and 3-D image quality metrics. 2-D networks and their 3-D counterparts were compared to assess the differences between enhancing volume slice by slice and enhancing it by 3-D patches. Furthermore, 3-D quantitative vascular metrics were conducted to quantify human retinal vasculature. Results: Freqformer substantially outperformed existing convolutional neural networks and Transformer-based methods, achieving superior image metrics. Importantly, the enhanced OCTA volumes show strong correlation with the merged volumes on vascular segment count, density, length, and flow index, further underscoring its reliability for quantitative vascular analysis. 3-D counterparts did not yield additional gains in image metrics or downstream 3-D vascular quantification but incurred nearly an order-of-magnitude longer inference time, supporting our 2-D slice-wise enhancement strategy. Additionally, Freqformer showed excellent generalization capability on larger field-of-view scans, surpassing the quality of conventional volumetric merging methods. Conclusion: Freqformer reliably generates high-definition 3-D retinal microvasculature from single-scan OCTA, enabling precise vascular quantification comparable to standard volumetric merging methods.
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