
Unsupervised KnowledgeTransfer for Learned Image Reconstruction
Deep learningbased image reconstruction approaches have demonstrated im...
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Graph Convolutional Networks for ModelBased Learning in Nonlinear Inverse Problems
The majority of modelbased learned image reconstruction methods in medi...
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An efficient QuasiNewton method for nonlinear inverse problems via learned singular values
Solving complex optimization problems in engineering and the physical sc...
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Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, manage...
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Quantifying Sources of Uncertainty in Deep LearningBased Image Reconstruction
Image reconstruction methods based on deep neural networks have shown ou...
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Fusing electrical and elasticity imaging
Electrical and elasticity imaging are promising modalities for a suite o...
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Deep Learning in Photoacoustic Tomography: Current approaches and future directions
Biomedical photoacoustic tomography, which can provide high resolution 3...
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On the unreasonable effectiveness of CNNs
Deep learning methods using convolutional neural networks (CNN) have bee...
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Blind hierarchical deconvolution
Deconvolution is a fundamental inverse problem in signal processing and ...
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Image reconstruction in dynamic inverse problems with temporal models
The paper surveys variational approaches for image reconstruction in dyn...
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Sequentially optimized projections in Xray imaging
This work applies Bayesian experimental design to selecting optimal proj...
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Learning and correcting nonGaussian model errors
All discretized numerical models contain modelling errors  this reality...
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Joint Reconstruction and LowRank Decomposition for Dynamic Inverse Problems
A primary interest in dynamic inverse problems is to identify the underl...
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On Learned Operator Correction
We discuss the possibility to learn a datadriven explicit model correct...
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Rapid WholeHeart CMR with Single Volume Superresolution
Background: Threedimensional, whole heart, balanced steady state free p...
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MultiScale Learned Iterative Reconstruction
Modelbased learned iterative reconstruction methods have recently been ...
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Networks for Nonlinear Diffusion Problems in Imaging
A multitude of imaging and vision tasks have seen recently a major trans...
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Approximate kspace models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a...
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Revealing cracks inside conductive bodies by electric surface measurements
An algorithm is introduced for using electrical surface measurements to ...
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Realtime Cardiovascular MR with Spatiotemporal Dealiasing using Deep Learning  Proof of Concept in Congenital Heart Disease
PURPOSE: Realtime assessment of ventricular volumes requires high accel...
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Spatiotemporal Deep Dealiasing for Prospective Assessment of Realtime Ventricular Volumes
PURPOSE: Realtime assessment of ventricular volumes requires high accel...
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Deep Dbar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
The mathematical problem for Electrical Impedance Tomography (EIT) is a ...
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Model based learning for accelerated, limitedview 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have sh...
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Andreas Hauptmann
verfied profile
Assistant Professor of Computational Mathematics and Inverse Problems at the University of Oulu, Academy Research Fellow (Academy of Finland), and Research Associate at the Centre for Medical Image Computing, UCL.
Interested in all things of Inverse Problems and Learned Image Reconstruction in Computational and Medical Imaging.