9 papers
A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models
Weijie Gan, Xucheng Wang, Tongyao Wang +6
Image reconstruction and image synthesis are important for handling incomplete multimodal imaging data, but existing methods require various task-specific models, complicating trai…
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
Huidong Xie, Weijie Gan, Reimund Bayerlein +24
Reducing scan times, radiation dose, and enhancing image quality for lower-performance scanners, are critical in low-dose PET imaging. Deep learning techniques have been investigat…
Plug-and-Play Posterior Sampling for Blind Inverse Problems
Anqi Li, Weijie Gan, Ulugbek S. Kamilov
We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are…
Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning
Chicago Y. Park, Weijie Gan, Zihao Zou +3
Model-based deep learning (MBDL) is a powerful methodology for designing deep models to solve imaging inverse problems. MBDL networks can be seen as iterative algorithms that estim…
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Xiaojian Xu, Weijie Gan, Satya V. V. N. Kothapalli +2
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately…
A Self-supervised Diffusion Bridge for MRI Reconstruction
Harry Gao, Weijie Gan, Yuyang Hu +2
Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reco…