Metal artifact reduction in spiral fan-beam CT using a new sinogram segmentation scheme

Objective of this study is to present and test a new method for metal artifact reduction (MAR) by segmenting raw CT data (sinogram). The artifact suppression technique incorporates two steps namely, metal projection segmentation in the sinogram and replacement of segmented regions by new values using an interpolation method. The proposed segmentation algorithm uses the sinogram instead of reconstructed CT slices. First, one of the best and newest region-based geometric active contour models is used to detect projection data affected by metal objects (missing projections). Then, the Hough-transform method is applied to detect all sinusoidal-like curves belonging to metal objects. Finally, a post image processing technique is used aiming to increase accuracy of the segmentation process. To provide a proof of performance, CT data of two patients with metallic teeth filling and pelvis prosthesis were included in the study as well as CT data of a phantom with metallic teeth inserts. Accuracy was determined by comparing mean, variance, mean squared error (MSE) and, peak signal to noise ratio (PSNR) as evaluation measurements of distortion in phantom images with respect to metallic teeth (original and suppressed) and without metallic teeth inserts. Quantitative results showed an average improvement of 12 dB in terms of PSNR and 517 in terms of MSE when the new MAR method was applied to remove metal artifacts. Qualitative improvement was also assessed by comparing uncorrected clinical images with artifact suppressed images. Moreover, qualitative comparison of the results of the proposed new method with the existing methods of MAR showed the superiority of the new method tested in this study.