  Deep neural network based methods have achieved promising results for CT
metal artifact reduction (MAR), most of which use many synthesized paired
images for training. As synthesized metal artifacts in CT images may not
accurately reflect the clinical counterparts, an artifact disentanglement
network (ADN) was proposed with unpaired clinical images directly, producing
promising results on clinical datasets. However, without sufficient
supervision, it is difficult for ADN to recover structural details of
artifact-affected CT images based on adversarial losses only. To overcome these
problems, here we propose a low-dimensional manifold (LDM) constrained
disentanglement network (DN), leveraging the image characteristics that the
patch manifold is generally low-dimensional. Specifically, we design an LDM-DN
learning algorithm to empower the disentanglement network through optimizing
the synergistic network loss functions while constraining the recovered images
to be on a low-dimensional patch manifold. Moreover, learning from both paired
and unpaired data, an efficient hybrid optimization scheme is proposed to
further improve the MAR performance on clinical datasets. Extensive experiments
demonstrate that the proposed LDM-DN approach can consistently improve the MAR
performance in paired and/or unpaired learning settings, outperforming
competing methods on synthesized and clinical datasets.
