  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.
