105 citations · 212 across the 22 of their papers we have counts for
7 papers · 1 filter
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Nadja Gruber, Johannes Schwab, Elke Gizewski +1
Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view C…
Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images
Matthias Schwab, Mathias Pamminger, Christian Kremser +3
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstructio…
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Andreas Kofler, Christian Wald, Tobias Schaeffter +2
The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or…
Feature reconstruction from incomplete tomographic data without detour
Simon Göppel, Jürgen Frikel, Markus Haltmeier
In this paper, we consider the problem of feature reconstruction from incomplete x-ray CT data. Such problems occurs, e.g., as a result of dose reduction in the context medical ima…
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
Andreas Kofler, Marc Dewey, Tobias Schaeffter +2
In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shall…
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter +4
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Lear…