Prior Image-Constrained Reconstruction using Style-Based Generative Models. - Varun A. Kelkar; Mark A. Anastasio (ICML, 2021)

SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. - Jason L. Granstedt; Varun A. Kelkar; Weimin Zhou; Mark A. Anastasio (Image Processing, 2021)

Assessing regularization in tomographic imaging via hallucinations in the null space. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Task-based evaluation of deep image super-resolution in medical imaging. - Varun A. Kelkar; Xiaohui Zhang 012; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Impact of deep learning-based image super-resolution on binary signal detection. - Xiaohui Zhang 012; Varun A. Kelkar; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (CoRR, 2021)

Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction. - Varun A. Kelkar; Sayantan Bhadra; Mark A. Anastasio (IEEE Trans. Computational Imaging, 2021)

On Hallucinations in Tomographic Image Reconstruction. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

Application of DatasetGAN in medical imaging: preliminary studies. - Zong Fan; Varun A. Kelkar; Mark A. Anastasio; Hua Li 003 (Image Processing, 2022)

Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Kyle J. Myers; Prabhat K. C.; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Prior image-based medical image reconstruction using a style-based generative adversarial network. - Varun A. Kelkar; Mark A. Anastasio (CoRR, 2022)

Sparsity-Driven Joint Blind Deconvolution-Demodulation with Application to Motor Fault Detection. - Varun A. Kelkar; Dehong Liu; Hiroshi Inoue; Makoto Kanemaru (ICASSP, 2023)

Evaluating generative stochastic image models using task-based image quality measures. - Varun A. Kelkar; Dimitrios S. Gotsis; Rucha Deshpande; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2023)

Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics. - Rucha Deshpande; Varun A. Kelkar; Dimitrios S. Gotsis; Prabhat K. C.; Rongping Zeng; Kyle J. Myers; Frank J. Brooks; Mark A. Anastasio (CoRR, 2024)

AmbientFlow: Invertible generative models from incomplete, noisy measurements. - Varun A. Kelkar; Rucha Deshpande; Arindam Banerjee 001; Mark A. Anastasio (Trans. Mach. Learn. Res., 2024)

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. - Ruiyang Zhao; Xi Peng 004; Varun A. Kelkar; Mark A. Anastasio; Fan Lam (IEEE Trans. Biomed. Eng., 2024)