Spectral CT metal artifact reduction using weighted masking and a One Step direct inversion reconstruction algorithm

This work investigates a metal artifact reduction technique that combines weighted masking of projection data corrupted by metal, improved photon-counting detector modeling, and the constrained ‘one-step’ spectral CT image reconstruction (cOSSCIR) algorithm. The cOSSCIR algorithm directly estimates the basis material maps from the photon counts data using an optimization algorithm that places constraints on the basis maps. The improved photon-counting detector spectral modeling improves the accuracy of the polyenergetic forward model, which is expected to reduce beam hardening artifacts due to metal. This study also explores weighting schemes to reduce the contribution of counts measurements corrupted by metal during reconstruction, including selective masking across energy window. Unlike two-step decomposition approaches, cOSSCIR does not require energy windows to be registered, thus enabling energy-selective masking of data corrupted by metal. Preliminary feasibility of the proposed methods was investigated through experimental photon-counting CT acquisition of a tissue specimen with metal inserts. The cOSSCIR algorithm estimated acrylic and aluminum basis maps which were combined to form a 50 keV effective monoenergetic image. The effective monoenergetic image reconstructed by cOSSCIR from all counts data demonstrated reduced streak and view aliasing artifacts compared to the reference filtered backprojection image. Weighting of the data corrupted by metal further reduced the remaining beam hardening artifacts, with weighted masking further reducing the streak artifacts.