Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Supervised reconstruction models are characteristically trained on matched pairs of
undersampled and fully-sampled data to capture an MRI prior, along with supervision …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …

Image reconstruction from undersampled k-space data has been playing an important role
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …

Recent style transfer models have provided promising artistic results. However, given a
photograph as a reference style, existing methods are limited by spatial distortions or …