Non-Contrast-Informed Low-Dose Multi-Phase Contrast-Enhanced CT Reconstruction via Local Optimal Transport

Multi-phase contrast-enhanced CT (CECT) is widely employed to capture the dynamic enhancement patterns and temporal evolution of organs and lesions. However, acquiring multiple phases increases radiation exposure and is inevitably accompanied by inter-phase misalignment and inconsistencies due to patient motion and the temporal variations in contrast uptake. Further dose reduction exacerbates noise and streak artifacts, severely degrading image quality and diagnostic reliability. In this work, we propose a novel reconstruction framework for multi-phase low-dose CECT that is guided by a routinely acquired non-contrast CT scan under weakly paired conditions. Specifically, the reconstruction model was formulated that explicitly separates common anatomical structures from phase-specific contrast variations and noise by deep dictionary representations. Then we employ a proximal gradient optimization method, analytically deriving its iterative procedure and unfolding it into an end-to-end trainable architecture, which preserves the theoretical interpretability of the model and facilitates efficient inference. To enhance structural alignment, we integrate local optimal transport to establish anatomically meaningful correspondences across phases, thereby enforcing structural fidelity and radio-density consistency. Extensive experiments on real clinical multi-phase datasets demonstrate that our method effectively suppresses noise and streak artifacts while recovering fine contrast-enhanced details. Both quantitative evaluation and expert clinical assessment confirm its superior performance compared with existing approaches. Moreover, downstream evaluation using the TotalSegmentator liver-lesion model shows substantial gains in hepatic tumor detectability under reduced-dose settings, enabling reliable lesion identification while significantly lowering radiation exposure. The codes and models are available at https://github.com/lixing0810/LOT-NCIRecon.