Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Bernhard, Stimpel"'
Autor:
Alexander Preuhs, Mario Bacher, Elisabeth Hoppe, Bernhard Stimpel, Andreas Maier, Jens Wetzl, Seung Su Yoon, Philipp Roser
Publikováno v:
IEEE Transactions on Medical Imaging. 40:2105-2117
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For
Autor:
Bernhard Stimpel, Jens Wetzl, Christoph Forman, Michaela Schmidt, Andreas Maier, Mathias Unberath
Publikováno v:
Journal of Imaging, Vol 4, Iss 11, p 124 (2018)
Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet str
Externí odkaz:
https://doaj.org/article/6f8688f20a1943f681289cdec94d1090
Autor:
Lasse Kling, Weilin Fu, Silke Christiansen, Frank Schebesch, Leonid Mill, Christopher Syben, Tobias Würfl, Andreas Maier, Mathis Hoffmann, Bernhard Stimpel
Publikováno v:
Nature Machine Intelligence. 1:373-380
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allow
Autor:
Stephan Seitz, Andreas Maier, Christopher Syben, Bernhard Stimpel, Markus Michen, Stefan B. Ploner
Publikováno v:
Medical Physics
Purpose Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a
Publikováno v:
ICIP
Image reconstruction is particularly difficult when the type of image degradations are unknown. This may be the case if the acquisition device is unknown or the images stem from an uncontrolled environment like the internet. Yet, it may be important
Publikováno v:
IEEE transactions on medical imaging. 39(11)
X-ray imaging is a wide-spread real-time imaging technique. Magnetic Resonance Imaging (MRI) offers a multitude of contrasts that offer improved guidance to interventionalists. As such simultaneous real-time acquisition and overlay would be highly fa
Autor:
Bernhard Stimpel, Alexander Preuhs, Andreas Maier, Philipp Roser, Christopher Syben, Marios Psychogios, Michael Manhart, Markus Kowarschik
Publikováno v:
Informatik aktuell ISBN: 9783658292669
Bildverarbeitung für die Medizin
Bildverarbeitung für die Medizin
High quality reconstruction with interventional C-arm conebeam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b8a04f828d95847741da4a382ee7657
https://doi.org/10.1007/978-3-658-29267-6_34
https://doi.org/10.1007/978-3-658-29267-6_34
Autor:
Andreas Maier, Bernhard Stimpel, Philipp Ritt, Tobias Würfl, A. Hans Vija, Maximilian P. Reymann, Michal Cachovan
Publikováno v:
2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Single Photon Emitted Computed Tomography (SPECT) is characterized by low photon counts which results in a high degree of image noise. In this work, we demonstrate the feasibility of applying deep learning-based image denoising networks to this imagi
Autor:
Andreas K, Maier, Christopher, Syben, Bernhard, Stimpel, Tobias, Würfl, Mathis, Hoffmann, Frank, Schebesch, Weilin, Fu, Leonid, Mill, Lasse, Kling, Silke, Christiansen
Publikováno v:
Nature machine intelligence
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allow
Autor:
Christopher Syben, Bernhard Stimpel, Tobias Würfl, Andreas Maier, Arnd Dörfler, Jonathan Lommen
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030129385
GCPR
GCPR
In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning. The network allows to learn the unknown operators in this conversion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a559a48ec99fb20cf4688bb9f474974
https://doi.org/10.1007/978-3-030-12939-2_35
https://doi.org/10.1007/978-3-030-12939-2_35