Zobrazeno 1 - 10
of 274
pro vyhledávání: '"Kolbitsch Christoph"'
Publikováno v:
Current Directions in Biomedical Engineering, Vol 4, Iss 1, Pp 263-266 (2018)
Quantitative native T1 Mapping of the myocardium without the application of contrast agents can be used to detect fibrosis in the left ventricle. Spatial resolution of standard native T1 mapping is limited by cardiac motion and hence is not sufficien
Externí odkaz:
https://doaj.org/article/46f97eef386b49adb1ac2bad471c3c59
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
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
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
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
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
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
Autor:
Chen Zhong, Kolbitsch Christoph, Smink Jouke, Harrison James, Puntmann Valentina O, Nagel Eike, Razavi Reza, Rinaldi Aldo, Schaeffter Tobias
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 14, Iss Suppl 1, p P256 (2012)
Externí odkaz:
https://doaj.org/article/4e96498a61bb406a9daf5b9df637d9a8
Autor:
Viezzer, Darian, Hadler, Thomas, Gröschel, Jan, Ammann, Clemens, Blaszczyk, Edyta, Kolbitsch, Christoph, Hufnagel, Simone, Kranzusch-Groß, Riccardo, Lange, Steffen, Schulz-Menger, Jeanette
Publikováno v:
In eBioMedicine April 2024 102
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