  Computed tomography (CT) is an imaging modality widely used for medical
diagnosis and treatment. CT images are often corrupted by undesirable artifacts
when metallic implants are carried by patients, which creates the problem of
metal artifact reduction (MAR). Existing methods for reducing the artifacts due
to metallic implants are inadequate for two main reasons. First, metal
artifacts are structured and non-local so that simple image domain enhancement
approaches would not suffice. Second, the MAR approaches which attempt to
reduce metal artifacts in the X-ray projection (sinogram) domain inevitably
lead to severe secondary artifact due to sinogram inconsistency. To overcome
these difficulties, we propose an end-to-end trainable Dual Domain Network
(DuDoNet) to simultaneously restore sinogram consistency and enhance CT images.
The linkage between the sigogram and image domains is a novel Radon inversion
layer that allows the gradients to back-propagate from the image domain to the
sinogram domain during training. Extensive experiments show that our method
achieves significant improvements over other single domain MAR approaches. To
the best of our knowledge, it is the first end-to-end dual-domain network for
MAR.
