294 citations · 928 across the 63 of their papers we have counts for
6 papers · 1 filter
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
Avrajit Ghosh, Michael T. Mccann, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems. The parameters of the regularizer ar…
Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
Zhishen Huang, Saiprasad Ravishankar
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements. Often sophisticated reconstruction algorithms are d…
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Zhishen Huang, Marc Klasky, Trevor Wilcox +1
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reco…
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky +1
In this work, we compare one-step and two-step approaches for X-ray computed tomography (CT) scatter correction and density reconstruction. X-ray CT is an important imaging techniq…
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…