Metal Artifact Reduction
12 papers with code • 0 benchmarks • 0 datasets
Metal artifact reduction aims to remove the artifacts introduced by metallic implants in CT images.
Benchmarks
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Latest papers with no code
View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT -- An Investigation of Potential Improvement
Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR).
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.
Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks
Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (CT).
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task.
Learning-Based Patch-Wise Metal Segmentation with Consistency Check
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions.
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
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.
Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT).
X-ray Photon-Counting Data Correction through Deep Learning
The simulated PCD data and the ground truth counterparts are then fed to a specially designed deep adversarial network for PCD data correction.
