5 papers
LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
Jigang Duan, Jiayi Wang, Heran Wang +3
Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artif…
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
Ligen Shi, Ping Yang, Chang Liu +3
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically. Th…
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…