9 March 2017 Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm
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Abstract
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.
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Taly Gilat Schmidt, Taly Gilat Schmidt, Rina Foygel Barber, Rina Foygel Barber, Emil Y. Sidky, Emil Y. Sidky, } "Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321B (9 March 2017); doi: 10.1117/12.2249079; https://doi.org/10.1117/12.2249079
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