2 citations · 2 across the 5 of their papers we have counts for
5 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…
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…
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…
Learning image from projection: a full-automatic reconstruction (FAR) net for sparse-views computed tomography
Genwei Ma, Yining Zhu, Xing Zhao
The sparse-views x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. However, in particular, the reconstructed image usually su…