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.
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).
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.
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.
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 • 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.
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.
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.
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.