Statistical Image Reconstruction for Inconsistent CT Projection Data

Abstract

The filtered backprojection is not able to cope with metal-induced inconsistencies in the Radon space which leads to artifacts in reconstructed CT images. A new algorithm is presented that reduces the drawbacks of existing artifact reduction strategies. Inconsistent projection data are bridged by directed interpolation. These projections are reconstructed using a weighted maximum likelihood algorithm (lambda-MLEM). The correlation coefficient between images of a torso phantom marked with steel markers reconstructed with lambda-MLEM and images of the same torso slice without markers quantifies the quality achieved. For clinical data, entropy maximization is presented to obtain appropriate weightings. Different interpolation strategies have been applied. The quality of reconstruction sensitively depends on the complexity of interpolation. A directional interpolation gives best results. However, the quality of the images can be further improved by an appropriate weighing within lambda-MLEM. This has been demonstrated with data from a torso phantom, a jaw with amalgam fillings and a hip prosthesis. lambda-MLEM image reconstruction using data from directional Radon space interpolation is a new approach for metal artifact reduction. The weighting in this statistical approach is used to reduce the influence of residual inconsistencies in a way that optimal artifact suppression is obtained by optimizing a compromise between residual inconsistencies and void data. The image quality is superior compared with other artifact reduction strategies.