no code implementations • 19 Sep 2023 • Rucha Deshpande, Muzaffer Özbey, Hua Li, Mark A. Anastasio, Frank J. Brooks
However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.
no code implementations • 2 Nov 2022 • Rucha Deshpande, Ashish Avachat, Frank J. Brooks, Mark A. Anastasio
In this work, a LBM was assessed for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.
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 • 24 Nov 2021 • Rucha Deshpande, Mark A. Anastasio, Frank J. Brooks
We designed several stochastic context models (SCMs) of distinct image features that can be recovered after generation by a trained GAN.
no code implementations • 27 Jun 2021 • Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system.
no code implementations • 30 Jan 2021 • Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio
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.
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 • 29 May 2020 • Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.
no code implementations • 26 Jan 2020 • Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged.