5 papers · 1 filter
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…
Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport
Muyu Liu, Chenhe Du, Xuanyu Tian +5
Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast…
Resolving Blind Inverse Problems under Dynamic Range Compression via Structured Forward Operator Modeling
Muyu Liu, Xuanyu Tian, Chenhe Du +3
Recovering radiometric fidelity from unknown dynamic range compression (UDRC), such as low-light enhancement and HDR reconstruction, is a challenging blind inverse problem, due to…
Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
Chenhe Du, Qing Wu, Xuanyu Tian +3
3D medical imaging is in high demand and essential for clinical diagnosis and scientific research. Currently, diffusion models (DMs) have become an effective tool for medical imagi…
Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
Chenhe Du, Xuanyu Tian, Qing Wu +4
Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors…