One-Step Material Decomposition Using Spectral Diffusion Posterior Sampling in Sparse-View Dual-Layer CT

Spectral X-ray imaging technology can enhance interventional imaging by providing material-specific information. However, typical interventional systems with cone beam CT (CBCT) face limitations in scan range and acquisition speed, making sparse-view imaging an attractive option for faster, lower-dose scans, although sparse data pose greater challenges for accurate material quantification. We previously proposed a Spectral Diffusion Posterior Sampling (Spectral DPS) framework for generalizable one-step material decomposition, which integrates a learned diffusion prior with a physics-based forward model. The approach requires only a single unconditional training while providing flexibility of application across different system configurations. In this work, we investigate the performance of Spectral DPS on a physical dual-layer CBCT system under different sparse-view conditions. Specifically, an anthropomorphic head phantom was scanned on a bench-top CBCT system equipped with a dual-layer flat-panel detector, and one-step material decomposition was performed using Spectral DPS with the number of projections ranging from 720 down to 45 views. Results demonstrate that Spectral DPS can achieve accurate water-bone separation, and maintains uniform soft tissue appearance even though such homogeneous regions represent an out-of-distribution condition. Decomposition results remain robust down to 120 views, with minimal loss of structural detail and quantitative accuracy. At more extreme sparsity, Spectral DPS exhibits increased variability and artifacts, though bony structures remain discernible at as few as 90 views. These findings highlight the potential of Spectral DPS to enable accurate and robust material decomposition under certain sparse-view acquisitions for interventional CBCT imaging.