23 citations · 43 across the 7 of their papers we have counts for
5 papers · 1 filter
On hallucinations in tomographic image reconstruction
Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks +1
Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-aft…
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction
Varun A. Kelkar, Sayantan Bhadra, Mark A. Anastasio
There remains an important need for the development of image reconstruction methods that can produce diagnostically useful images from undersampled measurements. In magnetic resona…
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the pe…
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio
Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is u…
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
The objective optimization of medical imaging systems requires full characterization of all sources of randomness in the measured data, which includes the variability within the en…