6 citations · 12 across the 3 of their papers we have counts for
6 papers
Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks
Chi Nok Enoch Kan, Najibakram Maheenaboobacker, Dong Hye Ye
Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a chall…
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation
Amirkoushyar Ziabari, Dong Hye Ye, Somesh Srivastava +3
While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown to have better image quality than Filtered Back Projection (FBP), its use has been limited by its high…
Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT
Amirkoushyar Ziabari, Dong Hye Ye, Lin Fu +4
With the recent introduction of CT scanners with large cone angles, wide coverage detectors now provide a desirable scanning platform for cardiac CT that allows whole heart imaging…
Deep Back Projection for Sparse-View CT Reconstruction
Dong Hye Ye, Gregery T. Buzzard, Max Ruby +1
Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data. FBP is computationally efficient but produces lower quality reconstructions tha…
A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)
G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic +3
Sparse sampling schemes have the potential to dramatically reduce image acquisition time while simultaneously reducing radiation damage to samples. However, for a sparse sampling s…
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT
Ruoqiao Zhang, Dong Hye Ye, Debashish Pal +3
Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective wa…