1 code implementation • 1 Feb 2021 • Andreas Kofler, Markus Haltmeier, Tobias Schaeffter, Christoph Kolbitsch
The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy.
1 code implementation • 1 Apr 2019 • Andreas Kofler, Marc Dewey, Tobias Schaeffter, Christian Wald, Christoph Kolbitsch
Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset.
no code implementations • 19 Dec 2019 • Andreas Kofler, Markus Haltmeier, Tobias Schaeffter, Marc Kachelrieß, Marc Dewey, Christian Wald, Christoph Kolbitsch
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.
no code implementations • 10 Feb 2020 • Andreas Kofler, Marc Dewey, Tobias Schaeffter, Christoph Kolbitsch, Markus Haltmeier
We compare the proposed reconstruction scheme to two ground truth-free reconstruction methods, namely a well known Total Variation (TV) minimization and an unsupervised adaptive Dictionary Learning (DIC) method.
1 code implementation • 4 Mar 2022 • Andreas Kofler, Christian Wald, Tobias Schaeffter, Markus Haltmeier, Christoph Kolbitsch
Iterative neural networks - which contain the physical model - can overcome these issues.
1 code implementation • 9 Jun 2022 • Andreas Kofler, Christian Wald, Tobias Schaeffter, Markus Haltmeier, Christoph Kolbitsch
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems.
no code implementations • 19 Jun 2023 • Felix F Zimmermann, Christoph Kolbitsch, Patrick Schuenke, Andreas Kofler
While various learned and non-learned approaches have been proposed, the existing learned methods fail to fully exploit the prior knowledge about the underlying MR physics, i. e. the signal model and the acquisition model.
no code implementations • 7 Aug 2023 • Andreas Kofler, Kirsten Miriam Kerkering, Laura Göschel, Ariane Fillmer, Cristoph Kolbitsch
Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI).
1 code implementation • 27 Sep 2023 • Felix Frederik Zimmermann, Andreas Kofler
We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling.