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Found 6 papers, 0 papers with code
Realistic Restorer: artifact-free flow restorer(AF2R) for MRI motion artifact removal
In this work, we incorporate the artifact generation mechanism to reestablish the relationship between artifacts and anatomical content in the image domain, highlighting the superiority of explicit models over implicit models in medical problems.
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.
Active CT Reconstruction with a Learned Sampling Policy
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.
Label-Removed Generative Adversarial Networks Incorporating with K-Means
Generative Adversarial Networks (GANs) have achieved great success in generating realistic images.
