activity
20172025
most citedSpatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data

87 citations · 196 across the 12 of their papers we have counts for

collaborators

12 papers

physics.med-ph2025★ 1 cited

PET Rapid Image Reconstruction Challenge (PETRIC)

Casper da Costa-Luis, Matthias J. Ehrhardt, Christoph Kolbitsch +4

Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (…

eess.IV2023★ 5 cited

Quantitative MR Image Reconstruction using Parameter-Specific Dictionary Learning with Adaptive Dictionary-Size and Sparsity-Level Choice

Andreas Kofler, Kirsten Miriam Kerkering, Laura Göschel +2

Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter-maps di…

eess.IV2023★ 14 cited

PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction

Felix F Zimmermann, Christoph Kolbitsch, Patrick Schuenke +1

Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed invers…

math.OC2023

Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…

math.OC2023

Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling

Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6

We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…

eess.IV2022

Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks

Andreas Kofler, Christian Wald, Tobias Schaeffter +2

Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems. The involved sparsifying t…