Data-Driven Multi-keV Virtual Monoenergetic Images Generation From Single-Energy CT Guided by Image-Domain Material Decomposition

Virtual monoenergetic images (VMIs), reconstructed from dual-energy CT (DECT) by capturing photon attenuation data at two distinct energy levels, can reduce beam-hardening artifacts and provide more quantitatively accurate attenuation measurements. Data-driven deep learning approaches have demonstrated the feasibility of synthesizing VMIs from conventional single-energy CT (SECT) scans. However, the lack of incorporation of physics-related information in such methods compromises their interpretability and robustness. Here we propose a novel hybrid data-driven framework that synergizes convolutional neural networks with physics-based material decomposition derived from DECT principles. This approach directly yields high-quality VMIs across various keV levels from SECT acquisitions. Through rigorous validation on 130 clinical cases spanning diverse anatomical regions and pathological conditions, our method demonstrates significant improvements over conventional purely data-driven approaches, as evidenced by enhanced anatomical visualization and superior performance on quantitative metrics. By eliminating dependence on DECT hardware while maintaining computational efficiency and incorporating physics-guided constraints, our framework leverages the widespread availability of SECT to provide a cost-effective, high-performance solution for diagnostic imaging in routine clinical practice.