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.
In recent years, there has been a resurgence of interest towards spectral computed tomography (CT) driven by a growing demand in photon-counting detectors. In performing spectral CT scanning, a practical issue is to accurately calibrate the spectral response of the X-ray imaging system. Mis-calibrated detector elements can lead to strong ring artifacts in the reconstructed tomographic image. For the purpose of modeling the spectral response, we propose a Gaussian blur model combined with the prior information on the X-ray spectra that accurately predicts the transmission curve and at the same time recovers realistic estimate of the spectra. This proposed method uses a low dimensional representation of the X-ray spectra by enforcing a sparsity constraint on the parameters of the Gaussian blur model. These parameters are estimated by formulating a constrained optimization problem, and two algorithms are suggested to solve such problem in an efficient way. The effectiveness of the model is evaluated on the simulated transmission measurements of known thicknesses of known materials. The performance of the two algorithms are also compared through the error between estimated and model X-ray spectra and the error between the predicted and simulated transmission curves.
Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained ‘One-Step’ Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.