92 citations · 241 across the 20 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…
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Il Yong Chun, Xuehang Zheng, Yong Long +1
Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training da…
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…
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…