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Found 9 papers, 3 papers with code
Total Deep Variation for Linear Inverse Problems
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term.
Total Deep Variation: A Stable Regularizer for Inverse Problems
In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer.
Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs
Our numerical experiments show that conditional GANs are suitable for generating images at different GBCA dose levels and can be used to augment datasets for virtual contrast models.
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped before converging to a stationary point.
Shared Prior Learning of Energy-Based Models for Image Reconstruction
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning.
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach
The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently.
Shape of my heart: Cardiac models through learned signed distance functions
The efficient construction of an anatomical model is one of the major challenges of patient-specific in-silico models of the human heart.
Gadolinium dose reduction for brain MRI using conditional deep learning
Recently, deep learning (DL)-based methods have been proposed for the computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects while preserving diagnostic value.
