LAUSR

PURPOSE Metal artifacts severely degrade CT image quality in clinical diagnosis, which are difficult to removed, especially for the beam hardening artifacts. The metal artifact reduction (MAR) based on prior images are the most frequently-used methods. However, there exists a lot misclassification in most prior images caused by absence of prior information such as the spectrum distribution of X-ray beam source, especially many or big metal included. The purpose of this work is to find a more accurate prior image to improve image quality. METHODS The proposed method comprise of following four steps. First, the metal image is segmented by thresholding an initial image, where the metal traces are identified in the initial projection data using the forward projection of the metal image. Second, the accurate absorbent model of certain metal image is calculated according to the spectrum distribution of certain X-ray beam source and energy-dependent attenuation coefficients of metal. Then, a new metal image is reconstructed by the general analytical reconstruction algorithm such as filtered back projection (FPB). The prior image is obtained by segmenting the difference image between the initial image and the new metal image into air, tissue and bone. Finally, the initial projection data are normalized by dividing the projection data of prior image pixel to pixel, the corrected image is obtained by interpolation, denormalization and reconstruction. RESULTS Some clinical images with dental fillings and knee prostheses are used to evaluate the proposed algorithm and normalized metal artifact reduction (NMAR) and linear interpolation (LI) method. The results demonstrate the artifacts can be reduced efficiently by the proposed method. CONCLUSIONS The proposed method could obtain an exact prior image using the prior information about X-ray beam source and energy-dependent attenuation coefficients of metal. As a result, the better performance of reducing beam hardening artifacts can be improved, even though there were many or big implants. Moreover, the process of the proposed method is rather simple and little extra calculation burden is necessary. It has superiorities over other algorithms when include big or many implants.