most cited3D-Imaging and Quantification of Magnetic Nanoparticle Uptake by Living Cells

77 citations · 77 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2020

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Nils Strodthoff, Patrick Wagner, Tobias Schaeffter +1

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. The progress in the…

eess.IV2020

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…

eess.IV2020

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…

physics.med-ph201977 cited

3D-Imaging and Quantification of Magnetic Nanoparticle Uptake by Living Cells

Hendrik Paysen, Norbert Loewa, Anke Stach +7

Magnetic particle imaging (MPI) is a non-invasive, non-ionizing imaging technique for the visualization and quantification of magnetic nanoparticles (MNPs). The technique is especi…

eess.IV2019

Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data

Andreas Kofler, Marc Dewey, Tobias Schaeffter +2

In this work we reduce undersampling artefacts in two-dimensional () golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $…