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One of the advantages of spectral computed tomography (CT) is it can achieveaccurate material components using the material decomposition methods. Theimage-based material decomposition is a common method to obtain specificmaterial components, and it can be divided into two steps: image reconstructionand post material decomposition. To obtain accurate material maps, the imagereconstruction method mainly focuses on improving image quality byincorporating regularization priors. Very recently, the regularization priorsare introduced into the post material decomposition procedure in the iterativeimage-based methods. Since the regularization priors can be incorporated intoimage reconstruction and post image-domain material decomposition, theperformance of regularization by combining these two cases is still an openproblem. To realize this goal, the material accuracy from those steps are firstanalyzed and compared. Then, to further improve the accuracy of decompositionmaterials, a two-step regularization based method is developed by incorporatingpriors into image reconstruction and post material decomposition. Bothnumerical simulation and preclinical mouse experiments are performed todemonstrate the advantages of the two-step regularization based method inimproving material accuracy.

Improved Material Decomposition with a Two-step Regularization for spectral CT