Optical Doppler Tomography (ODT) is a blood flow imaging technique popularly used in bioengineering applications. The fundamental unit of ODT is the 1D frequency response along the A-line (depth), named raw A-scan. A 2D ODT image (B-scan) is obtained by first sensing raw A-scans along the B-line (width), and then constructing the B-scan from these raw A-scans via magnitude-phase analysis and post-processing. To obtain a high-resolution B-scan with a precise flow map, densely sampled A-scans are required in current methods, causing both computational and storage burdens. To address this issue, in this paper we propose a novel sparse reconstruction framework with four main sequential steps: 1) early magnitude-phase fusion that encourages rich interaction of the complementary information in magnitude and phase, 2) State Space Model (SSM)-based representation learning, inspired by recent successes in Mamba and VMamba, to naturally capture both the intra-A-scan sequential information and between-A-scan interactions, 3) an Inception-based Feedforward Network module (IncFFN) to further boost the SSM-module, and 4) a B-line Pixel Shuffle (BPS) layer to effectively reconstruct the final results. In the experiments on real-world animal data, our method shows clear effectiveness in reconstruction accuracy. As the first application of SSM for image reconstruction tasks, we expect our work to inspire related explorations in not only efficient ODT imaging techniques but also generic image enhancement.

翻译：光学多普勒层析成像（ODT）是一种广泛应用于生物工程领域的血流成像技术。ODT的基本单元是沿A线（深度）的一维频率响应，称为原始A扫描。通过先沿B线（宽度）采集原始A扫描，再基于幅相分析与后处理从这些原始A扫描构建B扫描，从而获得二维ODT图像（B扫描）。现有方法为获得高分辨率且具有精确流图的B扫描，需要密集采样的A扫描，这造成了计算与存储的双重负担。为解决此问题，本文提出一种新颖的稀疏重建框架，包含四个主要顺序步骤：1）早期幅相融合，促进幅值与相位中互补信息的丰富交互；2）基于状态空间模型（SSM）的表征学习，受近期Mamba与VMamba成功案例的启发，自然捕捉A扫描内部序列信息及A扫描间交互信息；3）基于Inception的前馈网络模块（IncFFN），进一步增强SSM模块性能；4）B线像素混洗（BPS）层，有效重建最终结果。在真实动物数据实验中的结果表明，该方法在重建精度方面具有显著有效性。作为SSM在图像重建任务中的首次应用，期望本研究不仅能推动高效ODT成像技术的发展，也能为通用图像增强领域提供相关探索启示。