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.
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Latest papers with no code
MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images
Cone Beam Computed Tomography (CBCT) plays a key role in dental diagnosis and surgery.
Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging
This study introduces a novel reconstruction method for dental cone-beam computed tomography (CBCT), focusing on effectively reducing metal-induced artifacts commonly encountered in the presence of prevalent metallic implants.
Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction
However, it is difficult for previous unsupervised methods to retain structural information from CT images while handling the non-local characteristics of metal artifacts.
RetinexFlow for CT metal artifact reduction
Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult.
Metal-conscious Embedding for CBCT Projection Inpainting
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images.
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction
Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR).
Metal Inpainting in CBCT Projections Using Score-based Generative Model
During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems.
Deep learning based projection domain metal segmentation for metal artifact reduction in cone beam computed tomography
We compare our model's performance on real clinical scans with conventional region growing threshold-based MAR, moving metal artifact reduction method, and a recent deep learning method.
Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling
To improve the learning ability, the proposed network is designed to take advantage of the intra-oral scan data as side-inputs and perform multi-task learning of auxiliary tooth segmentation.
A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms
We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR).