Výsledky vyhledávání - "Kolbitsch, P"

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

Autor: Posselt, Christiane, Avci, Mehmet Yigit, Yigitsoy, Mehmet, Schünke, Patrick, Kolbitsch, Christoph, Schäffter, Tobias, Remmele, Stefanie

Purpose: To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid attenuated

Externí odkaz: http://arxiv.org/abs/2311.01894

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

Autor: Kofler, Andreas, Kerkering, Kirsten Miriam, Göschel, Laura, Fillmer, Ariane, Kolbitsch, Cristoph

Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method w

Externí odkaz: http://arxiv.org/abs/2308.03460

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

Autor: Zimmermann, Felix F, Kolbitsch, Christoph, Schuenke, Patrick, Kofler, Andreas

Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed inverse problem to obtain the desired tissue parameter maps from acquired ra

Externí odkaz: http://arxiv.org/abs/2306.11023

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

Autor: Kofler, Andreas, Altekrüger, Fabian, Ba, Fatima Antarou, Kolbitsch, Christoph, Papoutsellis, Evangelos, Schote, David, Sirotenko, Clemens, Zimmermann, Felix Frederik, Papafitsoros, Kostas

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 on two distinct sub-networks, with the first sub-network estimating the

Externí odkaz: http://arxiv.org/abs/2304.08350

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

Autor: Kofler, Andreas, Altekrüger, Fabian, Ba, Fatima Antarou, Kolbitsch, Christoph, Papoutsellis, Evangelos, Schote, David, Sirotenko, Clemens, Zimmermann, Felix Frederik, Papafitsoros, Kostas

We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV)-minimization. Our approach is inspired by recent developments

Externí odkaz: http://arxiv.org/abs/2301.05888

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

Autor: Kofler, Andreas, Wald, Christian, Schaeffter, Tobias, Haltmeier, Markus, Kolbitsch, Christoph

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 transformations or dictionaries are typically either pre-trained using

Externí odkaz: http://arxiv.org/abs/2206.04447

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Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks

Autor: Kofler, Andreas, Wald, Christian, Schaeffter, Tobias, Haltmeier, Markus, Kolbitsch, Christoph

The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or adaptively trained during the reconstruction. Thereby, learning algori

Externí odkaz: http://arxiv.org/abs/2203.02166

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Akademický článek

Post-hoc standardisation of parametric T1 maps in cardiovascular magnetic resonance imaging: a proof-of-conceptResearch in context

Autor: Darian Viezzer, Thomas Hadler, Jan Gröschel, Clemens Ammann, Edyta Blaszczyk, Christoph Kolbitsch, Simone Hufnagel, Riccardo Kranzusch-Groß, Steffen Lange, Jeanette Schulz-Menger

Publikováno v: EBioMedicine, Vol 102, Iss , Pp 105055- (2024)

Summary: Background: In cardiovascular magnetic resonance imaging parametric T1 mapping lacks universally valid reference values. This limits its extensive use in the clinical routine. The aim of this work was the introduction of our self-developed M

Externí odkaz: https://doaj.org/article/b57e2bb3e0f54f1bb5b556935e6330ee

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

Autor: Kofler, Andreas, Haltmeier, Markus, Schaeffter, Tobias, Kolbitsch, Christoph

Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, beca

Externí odkaz: http://arxiv.org/abs/2102.00783

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