Polychromatic sinogram based material decomposition and reconstruction methods: integrate standard reference database...

This study develops a sinogram-based material decomposition (MD) framework for a dual-energy cone-beam micro-computed tomography (micro-CT) system, providing quantitative spectral imaging without specialized photon-counting detectors. This work aims to address challenges in material characterization, particularly for preclinicalin-vivoimaging. The methodology involves acquiring dual-energy projections (50 and 80 kVp) of a standardized reference phantom to calibrate a three-material known basis model. This calibration is performed in the image domain to ensure empirical accuracy, while the decomposition model is solved in the sinogram domain using a robust and computationally efficient Moore-Penrose pseudoinverse solver. By applying mass attenuation coefficients from a standard reference database to these basis maps, the framework synthesizes artifact-reduced virtual monochromatic images, photoelectric effect images (PEIs), and Compton scatter images (CSIs). Our results demonstrate that the framework, once calibrated, is highly generalizable and can be reliably applied to other phantoms and complexin-vivorat scans. The method achieved accurate, automated segmentation of skeletal structures from soft tissues inin-vivodata, which was quantitatively validated on phantoms with structural similarity index values approaching 1.0 within the object region. The synthesis successfully separated physical attenuation mechanisms, isolating bone-dominant signals in PEIs and soft-tissue signals in CSIs. In conclusion, this study validates an accessible and robust hybrid MD method that enables quantitative, multi-material analysis on conventional micro-CT systems, offering a practical tool for advanced preclinical research.