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07/18/2023
Convergent regularization in inverse problems and linear plug-and-play denoisers
Plug-and-play (PnP) denoising is a popular iterative framework for solvi...
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05/08/2023
Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging
Reconstruction of tomographic images from boundary measurements requires...
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04/04/2023
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
Learned iterative reconstructions hold great promise to accelerate tomog...
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01/24/2023
Sequential model correction for nonlinear inverse problems
Inverse problems are in many cases solved with optimization techniques. ...
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11/02/2022
Unsupervised denoising for sparse multi-spectral computed tomography
Multi-energy computed tomography (CT) with photon counting detectors (PC...
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08/18/2022
Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography
Diffuse optical tomography (DOT) is a severely ill-posed nonlinear inver...
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06/20/2022
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
In industrial applications it is common to scan objects on a moving conv...
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06/11/2022
Learned reconstruction with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success...
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11/18/2021
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Many interventional surgical procedures rely on medical imaging to visua...
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07/06/2021
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated im...
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03/28/2021
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
The majority of model-based learned image reconstruction methods in medi...
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12/14/2020
An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
Solving complex optimization problems in engineering and the physical sc...
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12/09/2020
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, manage...
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11/17/2020
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown ou...
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10/14/2020
Fusing electrical and elasticity imaging
Electrical and elasticity imaging are promising modalities for a suite o...
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09/16/2020
Deep Learning in Photoacoustic Tomography: Current approaches and future directions
Biomedical photoacoustic tomography, which can provide high resolution 3...
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07/29/2020
On the unreasonable effectiveness of CNNs
Deep learning methods using convolutional neural networks (CNN) have bee...
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07/22/2020
Blind hierarchical deconvolution
Deconvolution is a fundamental inverse problem in signal processing and ...
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07/20/2020
Image reconstruction in dynamic inverse problems with temporal models
The paper surveys variational approaches for image reconstruction in dyn...
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06/22/2020
Sequentially optimized projections in X-ray imaging
This work applies Bayesian experimental design to selecting optimal proj...
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05/29/2020
Learning and correcting non-Gaussian model errors
All discretized numerical models contain modelling errors - this reality...
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05/28/2020
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
A primary interest in dynamic inverse problems is to identify the underl...
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05/14/2020
On Learned Operator Correction
We discuss the possibility to learn a data-driven explicit model correct...
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12/22/2019
Rapid Whole-Heart CMR with Single Volume Super-resolution
Background: Three-dimensional, whole heart, balanced steady state free p...
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08/01/2019
Multi-Scale Learned Iterative Reconstruction
Model-based learned iterative reconstruction methods have recently been ...
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11/29/2018
Networks for Nonlinear Diffusion Problems in Imaging
A multitude of imaging and vision tasks have seen recently a major trans...
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07/09/2018
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a...
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07/04/2018
Revealing cracks inside conductive bodies by electric surface measurements
An algorithm is introduced for using electrical surface measurements to ...
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03/14/2018
Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease
PURPOSE: Real-time assessment of ventricular volumes requires high accel...
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03/14/2018
Spatio-temporal Deep De-aliasing for Prospective Assessment of Real-time Ventricular Volumes
PURPOSE: Real-time assessment of ventricular volumes requires high accel...
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11/08/2017
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
The mathematical problem for Electrical Impedance Tomography (EIT) is a ...
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08/31/2017