Related papers: Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

URL: http://arxiv.org/abs/2007.03882v1
Date: Wed, 8 Jul 2020 03:47:34 GMT
Title: Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Authors: Chuang Niu, Wenxiang Cong, Fenglei Fan, Hongming Shan, Mengzhou Li, Jimin Liang, Ge Wang
Abstract summary: An artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. We propose a low-dimensional manifold (LDM) constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold is generally low-dimensional. We show that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings.
Score: 17.01644053979103
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.