6 papers
SCMA: Structure-Conditioned and Metal-Aware Flow Matching for CT Metal Artifact Reduction
Heran Wang, Jianing Sun, Xu Jiang +3
In X-ray CT, metallic objects cause beam hardening, photon starvation, and scattering, leading to projection inconsistency, streaks, dark bands, and structural distortions that com…
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
Genwei Ma, Ting Luo, Ping Yang +1
Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equat…
Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising
Jigang Duan, Genwei Ma, Xu Jiang +3
Diffusion and flow-based generative models have shown strong potential for image restoration. However, image denoising under unknown and varying noise conditions remains challengin…
Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
Ligen Shi, Xu Jiang, YunZe Liu +4
Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading…
Iterative approach to reconstructing neural disparity fields from light-field data
Ligen Shi, Chang Liu, Xing Zhao +1
This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to a…
Ring Artifacts Correction Based on Global-Local Features Interaction Guidance in the Projection Domain
Yunze Liu, Congyi Su, Xing Zhao
Ring artifacts are common artifacts in CT imaging, typically caused by inconsistent responses of detector units to X-rays, resulting in stripe artifacts in the projection data. Und…