  In computer tomography, due to the presence of metal implants in the patient
body, reconstructed images will suffer from metal artifacts. In order to reduce
metal artifacts, metals are typically removed in projection images. Therefore,
the metal corrupted projection areas need to be inpainted. For deep learning
inpainting methods, convolutional neural networks (CNNs) are widely used, for
example, the U-Net. However, such CNNs use convolutional filter responses on
both valid and corrupted pixel values, resulting in unsatisfactory image
quality. In this work, partial convolution is applied for projection
inpainting, which only relies on valid pixels values. The U-Net with partial
convolution and conventional convolution are compared for metal artifact
reduction. Our experiments demonstrate that the U-Net with partial convolution
is able to inpaint the metal corrupted areas better than that with conventional
convolution.
