262 citations · 653 across the 26 of their papers we have counts for
31 papers
Design of Novel Loss Functions for Deep Learning in X-ray CT
Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare +4
Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of dat…
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman +2
Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low freque…
MBIR Training for a 2.5D DL network in X-ray CT
Obaidullah Rahman, Madhuri Nagare, Ken D. Sauer +4
In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection te…
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging
Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard +1
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP l…
Sparse-View CT Reconstruction using Recurrent Stacked Back Projection
Wenrui Li, Gregery T. Buzzard, Charles A. Bouman
Sparse-view CT reconstruction is important in a wide range of applications due to limitations on cost, acquisition time, or dosage. However, traditional direct reconstruction metho…
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Maliha Hossain, Balasubramanya T. Nadiga, Oleg Korobkin +7
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in num…