14 citations · 20 across the 9 of their papers we have counts for
10 papers
Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction
Taewon Yang, Jason Hu, Jeffrey A. Fessler +1
Diffusion models learn strong image priors that can be leveraged to solve inverse problems like medical image reconstruction. However, for real-world applications such as 3D Comput…
CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
Bowen Song, Zecheng Zhang, Zhaoxu Luo +6
Diffusion models have emerged as powerful tools for generative tasks, producing high-quality outputs across diverse domains. However, how the generated data responds to the initial…
Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction
Tao Hong, Zhaoyi Xu, Jason Hu +1
Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes…
Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problems
Jason Hu, Bowen Song, Jeffrey A. Fessler +1
Diffusion models have achieved excellent success in solving inverse problems due to their ability to learn strong image priors, but existing approaches require a large training dat…
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…
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
Bowen Song, Jason Hu, Zhaoxu Luo +2
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding m…
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…
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior
Zongyu Li, Jason Hu, Xiaojian Xu +2
Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements…