Search Results for author: Andreas Kofler

Found 6 papers, 4 papers with code

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

1 code implementation9 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.

Dictionary Learning Image Reconstruction

An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction

1 code implementation1 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.

Dictionary Learning Image Reconstruction

Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI

no code implementations10 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.

Dictionary Learning

Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction

no code implementations19 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.

Image Reconstruction SSIM

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

1 code implementation1 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.

Data Augmentation Dictionary Learning

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