4 papers
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
Jason Hu, Bowen Song, Xiaojian Xu +2
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and r…
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
Tao Hong, Xiaojian Xu, Jason Hu +1
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of…
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Xiaojian Xu, Marc Klasky, Michael T. McCann +2
Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inerti…
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Zongyu Li, Yixuan Jia, Xiaojian Xu +3
Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised l…