6 papers
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…
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…
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Xuanyu Tian, Lixuan Chen, Qing Wu +4
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inve…
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Qing Wu, Chenhe Du, Xuanyu Tian +3
Motion correction (MoCo) in radial MRI is a particularly challenging problem due to the unpredictability of subject movement. Current state-of-the-art (SOTA) MoCo algorithms often…