Hybrid Framework Combining Pre-Trained Supervised Image Priors with Neural Representation for Extremely Sparse-View CT Reconstruction (HYPER)
Extremely sparse-view CT benefits for reducing radiation dose while causing streak artifact when using the traditional filtered-back projection (FBP). We propose a new learning-based reconstruction method, named HYPER (HYbrid framework combining pre-trained s...
Poster Program · Diagnostic and Interventional Radiology Physics