Search Results for author:
Found 9 papers, 2 papers with code
AmbientFlow: Invertible generative models from incomplete, noisy measurements
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.
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction.
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
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.
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging.
Prior image-based medical image reconstruction using a style-based generative adversarial network
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.
Impact of deep learning-based image super-resolution on binary signal detection
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.
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science.
On hallucinations in tomographic image reconstruction
The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction
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.
