47 citations · 63 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Model-Based Iterative Reconstruction for One-Sided Ultrasonic Non-Destructive Evaluation
Hani Almansouri, Singanallur Venkatakrishnan, Charles Bouman +1
One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could aff…
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…
Deep neural networks for non-linear model-based ultrasound reconstruction
Hani Almansouri, S. V. Venkatakrishnan, Gregery T. Buzzard +2
Ultrasound reflection tomography is widely used to image large complex specimens that are only accessible from a single side, such as well systems and nuclear power plant containme…
SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks
Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier +3
In scanning microscopy based imaging techniques, there is a need to develop novel data acquisition schemes that can reduce the time for data acquisition and minimize sample exposur…