23 citations · 33 across the 9 of their papers we have counts for
11 papers
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics
Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis +5
The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. The goal of this challenge was…
AmbientFlow: Invertible generative models from incomplete, noisy measurements
Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee +1
Generative models have gained popularity for their potential applications in imaging science, such as image reconstruction, posterior sampling and data sharing. Flow-based generati…
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
Ruiyang Zhao, Xi Peng, Varun A. Kelkar +2
We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction. The subspace model imposes an expl…
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoratio…
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesi…
Application of DatasetGAN in medical imaging: preliminary studies
Zong Fan, Varun Kelkar, Mark A. Anastasio +1
Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging. DatasetGAN is a recently proposed framework based on modern…
Prior image-based medical image reconstruction using a style-based generative adversarial network
Varun A. Kelkar, Mark A. Anastasio
Computed medical imaging systems require a computational reconstruction procedure for image formation. In order to recover a useful estimate of the object to-be-imaged when the rec…
Impact of deep learning-based image super-resolution on binary signal detection
Xiaohui Zhang, Varun A. Kelkar, Jason Granstedt +2
Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed…
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Varun A. Kelkar, Mark A. Anastasio
Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning o…
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…