Metal Artifact Reduction

7 papers with code • 0 benchmarks • 0 datasets

Metal artifact reduction aims to remove the artifacts introduced by metallic implants in CT images.

Most implemented papers

Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning

mughanibu/DeepMAR 9 Apr 2019

The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts.

Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

liaohaofu/adn 5 Jun 2019

Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset.

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

liaohaofu/adn 3 Aug 2019

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.

DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

zjk1988/DAN-Net 16 Feb 2021

With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

hongwang01/indudonet 11 Sep 2021

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

hongwang01/indudonet_plus 23 Dec 2021

During the computed tomography (CT) imaging process, metallic implants within patients always cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis.

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

hongwang01/ACDNet 16 May 2022

By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i. e.,} a clear interpretability for the MAR task.