Optical Doppler Tomography (ODT) is an emerging blood flow analysis technique. A 2D ODT image (B-scan) is generated by sequentially acquiring 1D depth-resolved raw A-scans (A-line) along the lateral axis (B-line), followed by Doppler phase-subtraction analysis. To ensure high-fidelity B-scan images, current practices rely on dense sampling, which prolongs scanning time, increases storage demands, and limits the capture of rapid blood flow dynamics. Recent studies have explored sparse sampling of raw A-scans to alleviate these limitations, but their effectiveness is hindered by the conservative sampling rates and the uniform modeling of flow and background signals. In this study, we introduce a novel blood flow-aware network, named ASBA (A-line ROI State space model and B-line phase Attention), to reconstruct ODT images from highly sparsely sampled raw A-scans. Specifically, we propose an A-line ROI state space model to extract sparsely distributed flow features along the A-line, and a B-line phase attention to capture long-range flow signals along each B-line based on phase difference. Moreover, we introduce a flow-aware weighted loss function that encourages the network to prioritize the accurate reconstruction of flow signals. Extensive experiments on real animal data demonstrate that the proposed approach clearly outperforms existing state-of-the-art reconstruction methods.

翻译：光学多普勒断层扫描（ODT）是一种新兴的血流分析技术。二维ODT图像（B扫描）通过沿横向轴（B线）顺序采集一维深度分辨原始A扫描（A线），随后进行多普勒相位差分分析生成。为确保高保真度的B扫描图像，现有方法依赖密集采样，这会延长扫描时间、增加存储需求，并限制快速血流动态的捕捉。近期研究探索通过原始A扫描的稀疏采样来缓解这些限制，但其效果受限于保守的采样率以及对血流与背景信号的统一建模。本研究提出一种新型血流感知网络ASBA（A线ROI状态空间模型与B线相位注意力），用于从高度稀疏采样的原始A扫描中重建ODT图像。具体而言，我们提出A线ROI状态空间模型以提取沿A线稀疏分布的血流特征，并设计B线相位注意力机制基于相位差捕捉沿每条B线的长程血流信号。此外，我们引入血流感知加权损失函数，促使网络优先保证血流信号的精确重建。在真实动物数据上的大量实验表明，所提方法显著优于现有的先进重建方法。