用多轴监督修复手持OCTA的运动伪影，提升血管连续性与图像质量。
VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes
- 采用2.5D U-Net结合血管感知多轴正交损失，联合优化跨断面与投影一致性。
- 在真实与合成数据上均优于现有方法，恢复出清晰毛细血管与完整血管连通性。
- 适合眼科医生用于儿科或不配合患者的手持OCTA影像后处理。
手持光学相干断层扫描血管成像（OCTA）可实现非侵入式视网膜成像，适用于不配合或儿童受试者，但极易受运动伪影影响，严重降低三维图像质量。3D采集过程中突发运动会导致整幅B-scan出现未采样区域，使终末投影中产生空白带。本文提出VAMOS-OCTA，一种基于血管感知多轴正交监督的深度学习框架，用于修复运动损坏的B-scan。采用2.5D U-Net架构，以邻近B-scan堆叠为输入，重建受损中心B-scan，通过新颖的血管感知多轴正交监督（VAMOS）损失进行引导。该损失结合血管加权强度重建与轴向及横向投影一致性，鼓励原生B-scan及正交平面上的血管连续性。不同于以往仅关注增强终末最大强度投影（MIP）的方法，VAMOS-OCTA同时提升横断面图像锐度与三维投影精度，即使在严重运动伪影下仍表现优异。模型在合成与真实损伤数据上训练并评估，使用感知质量与像素级准确率指标。结果表明，其持续优于现有方法，生成具备清晰毛细血管、恢复血管连续性且终末投影干净的重构图像。这证明多轴监督是恢复运动退化3D OCTA数据的强大约束。源代码已公开于 https://github.com/MedICL-VU/VAMOS-OCTA。
原文摘要 · Abstract (English)
Handheld Optical Coherence Tomography Angiography (OCTA) enables noninvasive retinal imaging in uncooperative or pediatric subjects, but is highly susceptible to motion artifacts that severely degrade volumetric image quality. Sudden motion during 3D acquisition can lead to unsampled retinal regions across entire B-scans (cross-sectional slices), resulting in blank bands in en face projections. We propose VAMOS-OCTA, a deep learning framework for inpainting motion-corrupted B-scans using vessel-aware multi-axis supervision. We employ a 2.5D U-Net architecture that takes a stack of neighboring B-scans as input to reconstruct a corrupted center B-scan, guided by a novel Vessel-Aware Multi-Axis Orthogonal Supervision (VAMOS) loss. This loss combines vessel-weighted intensity reconstruction with axial and lateral projection consistency, encouraging vascular continuity in native B-scans and across orthogonal planes. Unlike prior work that focuses primarily on restoring the en face MIP, VAMOS-OCTA jointly enhances both cross-sectional B-scan sharpness and volumetric projection accuracy, even under severe motion corruptions. We trained our model on both synthetic and real-world corrupted volumes and evaluated its performance using both perceptual quality and pixel-wise accuracy metrics. VAMOS-OCTA consistently outperforms prior methods, producing reconstructions with sharp capillaries, restored vessel continuity, and clean en face projections. These results demonstrate that multi-axis supervision offers a powerful constraint for restoring motion-degraded 3D OCTA data. Our source code is available at https://github.com/MedICL-VU/VAMOS-OCTA.
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