OA-Recon: an Online Adaptive Reconstruction Framework for Sparse-View Multi-Energy CT

Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radiation exposure, we propose a complementary sparse-view scanning protocol tailored for MECT, which reduces radiation dose while maximizing angular coverage. To reconstruct high-quality images from these sparse-view data and ensure algorithmic reliability in practical applications, we introduce an Online Adaptive Reconstruction (OA-Recon) framework that adapts robustly to varying acquisition settings through two designs. First, it adopts a Bayesian adaptation strategy for instance-specific optimization while preserving the learned prior. Second, it incorporates a Frequency-adaptive and Physics-informed Network (FaPiNet) for adaptive feature extraction and acquisition-conditioned feature modulation. In addition, it incorporates a spectral attention mechanism to fully exploit complementary information across energy channels. Experiments on simulated MECT and real mouse PCCT data show that FaPiNet-OA-Recon achieves better performance in suppressing streak artifacts, restoring image details, and maintaining CT-value accuracy. More importantly, OA-Recon demonstrates adaptability to changes in view, spectrum, and anatomy, providing a preliminarily feasible solution for clinical applications of MECT.