  In the presence of metal implants, metal artifacts are introduced to x-ray CT
images. Although a large number of metal artifact reduction (MAR) methods have
been proposed in the past decades, MAR is still one of the major problems in
clinical x-ray CT. In this work, we develop a convolutional neural network
(CNN) based open MAR framework, which fuses the information from the original
and corrected images to suppress artifacts. The proposed approach consists two
phases. In the CNN training phase, we build a database consisting of
metal-free, metal-inserted and pre-corrected CT images, and image patches are
extracted and used for CNN training. In the MAR phase, the uncorrected and
pre-corrected images are used as the input of the trained CNN to generate a CNN
image with reduced artifacts. To further reduce the remaining artifacts, water
equivalent tissues in a CNN image are set to a uniform value to yield a CNN
prior, whose forward projections are used to replace the metal-affected
projections, followed by the FBP reconstruction. The effectiveness of the
proposed method is validated on both simulated and real data. Experimental
results demonstrate the superior MAR capability of the proposed method to its
competitors in terms of artifact suppression and preservation of anatomical
structures in the vicinity of metal implants.
