6 citations · 12 across the 5 of their papers we have counts for
6 papers · 1 filter
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
William Herzberg, Daniel B. Rowe, Andreas Hauptmann +1
The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solve…
Deep Learning in Photoacoustic Tomography: Current approaches and future directions
Andreas Hauptmann, Ben Cox
Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from th…
On the unreasonable effectiveness of CNNs
Andreas Hauptmann, Jonas Adler
Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks wit…
Image reconstruction in dynamic inverse problems with temporal models
Andreas Hauptmann, Ozan Öktem, Carola Schönlieb
The paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on methods that rely on parametrised temporal models. These are here enco…
Rapid Whole-Heart CMR with Single Volume Super-resolution
Jennifer A. Steeden, Michael Quail, Alexander Gotschy +4
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have…
Multi-Scale Learned Iterative Reconstruction
Andreas Hauptmann, Jonas Adler, Simon Arridge +1
Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inver…