Paper Info
Title
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.
Abstract
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that disentangles the metal artifacts from CT images in the latent space. It supports different forms of generations (artifact reduction, artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https:// github.com/liaohaofu/adn</uri> .
Year
DOI
Venue
2020
10.1109/TMI.2019.2933425
IEEE transactions on medical imaging
Keywords
Field
DocType
Metals,Computed tomography,Decoding,Mars,X-ray imaging,Image reconstruction,Training
Metal Artifact,Pattern recognition,Source code,Computer science,Unsupervised learning,Artificial intelligence,Computed tomography,Artificial neural network
Journal
Volume
Issue
ISSN
39
3
0278-0062
Citations 
PageRank 
References 
6
0.48
11
Authors
4
Name
Order
Citations
PageRank
Haofu Liao1276.97
Lin Wei-An2345.32
Zhou S. Kevin347441.40
Jiebo Luo46314374.00