Metal Artifact Reduction by Direct Artifact Prediction using Deep Learning in X-ray CT

Metals included inside an object (such as clips, bolts, or artificial joints) cause streaks and dark band artifacts in computed tomography. Although several metal artifact reduction (MAR) methods have been reported, they require a large amount of processing time such as for iterative forward projection from reconstructed image, but they do not provide sufficient correction depending on the metal and object and are sometimes accompanied by image degradation due to new artifacts. To overcome these problems, MAR methods using artificial intelligence (AI-MAR) with deep learning are reported. We have developed new AI-MAR to directly predict artifacts using deep learning and subtract them from the original filtered back-projection (FBP) image to maintain the structure of the object and achieve a high artifact reduction effect. The proposed AI-MAR was compared with FBP, linear interpolation method (LI), and conventional MAR. In a metal simulation experiment, the proposed AI-MAR successfully reduced the metal artifacts, and the structural similarity indices (SSIMs) evaluated with the FBP, LI, conventional MAR, and proposed AI-MAR were 0.941, 0.986, 0.969, and 0.988, respectively, and an improvement rate of SSIM of more than 80% was demonstrated. The proposed AI-MAR can improve device performance by providing high-speed and highly accurate images with reduced metal artifacts.