3 papers
cs.CV2026
Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction
Qing Wu, Xuanyu Tian, Chenhe Du +4
Implicit neural representations (INRs) have emerged as a powerful paradigm for medical imaging via physics-informed unsupervised learning. Classical INRs optimize an entire network…
cs.CV2025
Diffusion Model Regularized Implicit Neural Representation for CT Metal Artifact Reduction
Jie Wen, Chenhe Du, Xiao Wang +1
Computed tomography (CT) images are often severely corrupted by artifacts in the presence of metals. Existing supervised metal artifact reduction (MAR) approaches suffer from perfo…
cs.CV2024
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
Qing Wu, Xu Guo, Lixuan Chen +8
X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coef…