Area of research
Radiology, Nuclear Medicine and Imaging · Radiation
Research interest
Research interests include Artificial intelligence, Computer science, Computer vision, Imaging phantom, Deconvolution, and Microscopy.
Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Three-dimensional structured illumination microscopy with enhanced axial resolution
Incorporating the image formation process into deep learning improves network performance
Blip up‐down acquisition for spin‐ and gradient‐echo imaging (<scp>BUDA‐SAGE</scp>) with self‐supervised denoising enables efficient <scp>T<sub>2</sub></scp>, <scp>T<sub>2</sub></scp>*, para‐ and dia‐magnetic susceptibility mapping
Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network
Rapid image deconvolution and multiview fusion for optical microscopy
Penalized-Likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
PET image denoising using unsupervised deep learning
Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture