21 citations · 73 across the 46 of their papers we have counts for
4 papers · 2 filters
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Xuehang Zheng, Saiprasad Ravishankar, Yong Long +2
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computation…
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray c…
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
Bihan Wen, Saiprasad Ravishankar, Luke Pfister +1
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS)…
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Zhipeng Li, Saiprasad Ravishankar, Yong Long +1
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition ope…