23 citations · 43 across the 7 of their papers we have counts for
8 papers · 1 filter
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
Sayantan Bhadra, Umberto Villa, Mark A. Anastasio
Tomographic imaging is in general an ill-posed inverse problem. Typically, a single regularized image estimate of the sought-after object is obtained from tomographic measurements.…
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +2
Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. Th…
Advancing the AmbientGAN for learning stochastic object models
Weimin Zhou, Sayantan Bhadra, Frank J. Brooks +3
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks. Varia…
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…