294 citations · 928 across the 63 of their papers we have counts for
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SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Zhipeng Li, Siqi Ye, Yong Long +1
Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized in…
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…