Metal artifact reduction based on fully convolutional networks in CT image domain

Metal objects inside the field of view would introduce severe artifacts in x-ray CT images, which would severely degrade the quality of CT data and bring huge difficulties for subsequent image processing and analysis. Correction of metal artifacts has become a hot and difficult issue in X-ray CT. In recent years, deep learning has rapidly gained attention for employment on image processing. In this study, we introduce a Fully Convolutional Networks (FCNs) into the MAR in image domain. The network reduces metal artifacts by learning an end-to-end mapping of images from metal-corrupted CT images to their corresponding artifact-free ground truth. The network takes the metal-corrupted CT images as the input and takes the artifact-free images as the target. The convolution layers extract features from the input images and map them to the target images, and the deconvolution layers use these features to build the predicted outputs. Experimental results demonstrate that the proposed method can well reduce metal artifacts of CT images, and take a shorter time to process the images than traditional method.