Metal Artifact Reduction in CT-Based Attenuation Correction of PET Data Using the Virtual Sinogram Concept

Abstract

The presence of high density materials in CT imaging is known to generate strong streak artifacts in CT images. This will obviously impact the generated mumaps, and as such could affect the reconstructed PET image during the CT-based attenuation correction (CTAC) procedure. Thus the attenuation maps (mumaps) generated from these images will likely propagate the artifacts to the resulting PET images. The main problem associated with current sinogram-based methods for metal artifact reduction is the difficulty in manipulating raw CT data which usually consist of large files saved in vendor's proprietary formats. The proposed method directly computes a virtual sinogram through forward projection of CT images to overcome the above mentioned cumbersome inconvenience. The metallic objects in the CT image are first segmented. This is followed by forward projecting the obtained metal-only image. Then, missing projection data affected by metallic objects are detected and replaced by interpolated values from neighboring data using the spline interpolation functions. The algorithm is applied to a polyethylene phantom scanned before and after insertion of metallic objects. The corrected and non-corrected mumaps are compared to artifact-free mumap. It was shown that the mean relative error in regions close to metallic objects is reduced by 30% after applying our method. In another experiment a Jaszczak phantom is used to evaluate the results of the algorithm on reconstructed PET images. The activity concentration error produced in PET images is reduced by 85%. Moreover, the reconstruction of attenuation correction factors shows an obvious reduction of metal artifacts in the generated mumaps after applying the proposed algorithm. This study reports results obtained from a limited set of experimental measurements, further evaluation using clinical data sets is ongoing.