Metal Artifact Reduction
The papers in this topic, the datasets they use, the teams that focus on it, the toolkits assigned to it, and the authors with several papers here. Select any node to see its connections.
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Metal Artifact Reduction
Team
- CT Imaging Group, Department of Engineering Physics
- I-STAR Lab (Imaging for Surgery, Therapy, and Radiology)
Topic
Paper
- A dual-stream deep convolutional network for reducing metal streak artifacts in CT images
- A Method for Correcting Bone Induced Artifacts in Computed Tomography Scanners
- ACDNet
- ADN
- AllFreqNet
- AMIFound
- Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
- CALIMAR-GAN
- CNN-MAR
- Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
- Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
- Convolutional neural network metal artifact reduction in ultra-high resolution photon counting detector CT of middle- and inner-ear implants
- Correction for beam hardening in computed tomography
- DAN-Net
- Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images
- Deep-MAR
- DICDNet
- Diffusion model-regularized implicit neural representation for computed tomography metal artifact reduction
- DuDoDp-MAR
- DuDoDR-Net
- DuDoNet
- DuDoUFNet
- EDA
- FID-Net
- FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
- FORCE
- H3D-MarNet
- IDOL-Net
- InDuDoNet
- InDuDoNet+
- Joint CT reconstruction of anatomy and implants using a mixed prior model
- KMAR-Net
- LDM-DN
- Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
- LWCDNet
- MAR-4Dec
- MEPNet
- Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework
- Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning
- Metal artifact reduction in 2D CT images with self-supervised cross-domain learning
- MGMAR
- NMAR
- OSCNet
- PBAC
- PDuMSRNet
- PND-Net
- Polyner
- PRAISE-Net
- Quad-Net
- Reduction of CT artifacts caused by metallic implants
- ReMAR-DS
- SemiMAR
- Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling
- Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling
- UDAMAR
- UniMedIR
- Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN
- Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks
- VoxelSynth3D
Toolkit
Dataset
Author
- Bo Zhou
- Bruno De Man
- Chi Liu
- Chuang Niu
- Deyu Meng
- Ge Wang
- Haimiao Zhang
- Haofu Liao
- Hengyong Yu
- Hong Wang
- Hongming Shan
- Hu Chen
- Huaiqiang Sun
- Inchul Kim
- James S. Duncan
- Jiebo Luo
- Kai Ma
- Kaichao Liang
- Kiseok Choi
- Lars Gjesteby
- Lei Xing
- Lequan Yu
- Marc Kachelrieß
- Min H. Kim
- S. Kevin Zhou
- Tao Wang
- Wenjun Xia
- Xiaomeng Li
- Yan Liu
- Yang Chen
- Yefeng Zheng
- Yi Zhang
- Yuexiang Li
- Yuxiang Xing
- Yuyao Zhang
- Zhan Wu
- Zhicheng Zhang