21 citations · 37 across the 9 of their papers we have counts for
20 papers
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
Ling Chen, Zhishen Huang, Yong Long +1
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image recons…
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
Xikai Yang, Zhishen Huang, Yong Long +1
The recently proposed sparsifying transform models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure revea…
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler +1
Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the num…
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction
Anish Lahiri, Guanhua Wang, Saiprasad Ravishankar +1
This paper examines a combined supervised-unsupervised framework involving dictionary-based blind learning and deep supervised learning for MR image reconstruction from under-sampl…
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Zhishen Huang, Siqi Ye, Michael T. McCann +1
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements. Model-base…
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
Michael T. McCann, Marc L. Klasky, Jennifer L. Schei +1
We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a train…