ASBA: A-line State Space Model and B-line Attention for Sparse Optical Doppler Tomography Reconstruction

2026-01-20

AI Summary

Optical Doppler tomography (ODT) suffers from prolonged scan times and substantial storage demands due to dense sampling, hindering the capture of rapid hemodynamic dynamics. Existing sparse reconstruction approaches are limited by low sampling rates and the unified modeling of blood flow and background signals. To address these challenges, this work proposes a flow-aware reconstruction network, ASBA, which introduces, for the first time, an A-line region-of-interest state-space model to extract sparsely distributed flow features along the depth dimension and integrates a B-line phase attention mechanism to capture long-range lateral flow signals. Additionally, a flow-aware weighted loss function is designed to prioritize hemodynamic information during training. The proposed method significantly outperforms current state-of-the-art techniques under highly sparse sampling conditions, achieving high-fidelity ODT reconstructions on in vivo animal data and enabling greater degrees of sampling sparsity.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Optical Doppler Tomography
• sparse sampling
• blood flow reconstruction
• A-line
• B-scan

Innovation

• State Space Model
• Phase Attention
• Sparse Sampling
• Optical Doppler Tomography
• Flow-aware Reconstruction