no code implementations • 3 May 2024 • Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis, Prabhat KC, Rongping Zeng, Kyle J. Myers, Frank J. Brooks, Mark A. Anastasio
The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics.
no code implementations • 9 Sep 2023 • Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio
In applications such as computed imaging, it is often difficult to acquire such data due to requirements such as long acquisition time or high radiation dose, while acquiring noisy or partially observed measurements of these objects is more feasible.
no code implementations • 14 Jun 2023 • Ruiyang Zhao, Xi Peng, Varun A. Kelkar, Mark A. Anastasio, Fan Lam
We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction.
no code implementations • 26 Apr 2022 • Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Prabhat KC, Kyle J. Myers, Rongping Zeng, Mark A. Anastasio
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment.
no code implementations • 7 Apr 2022 • Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Kyle J. Myers, Prabhat KC, Rongping Zeng, Mark A. Anastasio
However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging.
no code implementations • 17 Feb 2022 • Varun A. Kelkar, Mark A. Anastasio
Discrepancy between the sought-after and prior images is measured in the disentangled latent-space, and is used to regularize the inverse problem in the form of constraints on specific styles of the disentangled latent-space.
no code implementations • 6 Jul 2021 • Xiaohui Zhang, Varun A. Kelkar, Jason Granstedt, Hua Li, Mark A. Anastasio
The presented study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.
1 code implementation • 24 Feb 2021 • 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.
3 code implementations • 1 Dec 2020 • Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks, Mark A. Anastasio
The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
no code implementations • 5 Jul 2020 • Varun A. Kelkar, Sayantan Bhadra, Mark A. Anastasio
To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models.