Search Results for author: Christoph Kolbitsch

Found 9 papers, 4 papers with code

Simulation of acquisition shifts in T2 Flair MR images to stress test AI segmentation networks

no code implementations3 Nov 2023 Christiane Posselt, Mehmet Yigit Avci, Mehmet Yigitsoy, Patrick Schünke, Christoph Kolbitsch, Tobias Schäffter, Stefanie Remmele

Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art MS lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI).

Lesion Segmentation Segmentation

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

no code implementations19 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.

MRI Reconstruction

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

Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting

no code implementations1 May 2017 Jackie Ma, Maximilian März, Stephanie Funk, Jeanette Schulz-Menger, Gitta Kutyniok, Tobias Schaeffter, Christoph Kolbitsch

High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.

Anatomy Image Reconstruction

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