piwik-script

Deutsch Intern
  • Human sceleton (computed tomographic display)
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Publications

  • Jonas Kleineisel, Julius F. Heidenreich, Philipp Eirich, Nils Petri, Herbert Köstler, Bernhard Petritsch, Thorsten A. Bley, Tobias Wech
    Real-time cardiac MRI using undersampled spiral readouts and a reconstruction based on a Variational Network
    Magnetic Resonance in Medicine 2022, doi: 10.1002/mrm.29357.
  • Katja Lauer, Jonas Kleineisel, Alfio Borzì, Thorsten A. Bley, Herbert Köstler, Tobias Wech
    Assessment of resolution and noise in MR images reconstructed by data driven approaches
    ISMRM 2022 – Conference Abstract 0303
  • Tobias Wech, Julius Heidenreich, Thorsten A. Bley, Bettina Baeßler
    A disentangled representation trained for joint reconstruction and segmentation of radially undersampled cardiac MRI
    ISMRM 2022 – Conference Abstract 0016
  • Nadja Vater, Alfio Borzì
    Training Artificial Neural Networks with Gradient and Coarse-Level Correction Schemes
    Machine Learning, Optimization, and Data Science
    7th International Conference, Grasmere, UK, October 4–8, 2021.
    LOD 2021 Springer LNCS Conference Proceedings, LNCS 13163, pp. 473-487, 2022

    DOI: 10.1007/978-3-030-95467-3_34
  • Sebastian Hofmann, Alfio Borzì
    A sequential quadratic hamiltonian algorithm for training explicit RK neural networks
    In: Journal of Computational and Applied Mathematics, 2021 (Online ahead of print),
    DOI: 10.1016/j.cam.2021.113943
  • Tobias Wech, Markus Johannes Ankenbrand, Thorsten Alexander Bley, Julius Frederik Heidenreich
    A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series
    In: Magnetic Resonance in Medicine - Wiley Online Library. DOI: 10.1002/mrm.29017
  • Julius F Heidenreich, Tobias Gassenmaier, Markus J Ankenbrand, Thorsten A Bley, Tobias Wech
    Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction
    In: Eur J Radiol. 2021 Jun 9; Online ahead of print.; DOI: 10.1016/j.ejrad.2021.109817
  • Andreas M Weng, Julius F Heidenreich, Corona Metz, Simon Veldhoen, Thorsten A Bley, Tobias Wech
    Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times
    In: BMC Med Imaging. 2021 May 8;21(1):79. DOI: 10.1186/s12880-021-00608-1
  • J. Kleineisel, P. Eirich, J. F. Heidenreich, H. Köstler, T. A. Bley, and T. Wech
    Real-time cardiac MRI using spiral read-outs and a Variational Network for data-driven reconstruction
    In : Proceedings of the 29th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Conference Abstract 2870, May 2021.
    DOI: 10.1002/mrm.28621

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  18. K. BREININGER, S. ALBARQOUNI, T. KURZENDORFER, M. PFISTER, M. KOWARSCHIK, AND A. MAIER, Intraoperative stent segmentation in x-ray fluoroscopy for endovascular aortic repair, International Journal of Computer Assisted Radiology and Surgery, 13 (2018).
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  21. G. CHLEBUS, A. SCHENK, J. H. MOLTZ, B. VAN GINNEKEN, H. K. HAHN, AND H. MEINE, Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based processing, Scientific Reports, 8 (2018), p. 15497.
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  37. K. HAMMERNIK, T. WÜRFL, T. POCK, AND A. MAIER, A deep learning architecture for limited-angle computed tomography reconstruction, in Bildverarbeitung für Medizin 2017, Springer Berlin Heidelberg, 2017, pp. 92–97.
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  42. Y. HUANG, T. WÜRFL, K. BREININGER, L. LIU, G. LAURITSCH, AND A. MAIER, Some investigations on robustness of deep learning in limited angle tomography, in A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-Lopez, G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Springer International Publishing, Cham, 2018, pp. 991–1048.
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  53. P. MOESKOPS, M. A. VIERGEVER, A. M. MENDRIK, L. S. DE VRIES, M. J. BENDERS, AND I. ISGUM, Automatic segmentation of mr brain images with a convolutional neural network, IEEE transactions on medical imaging, 35 (2016), pp. 1252–61.
  54. I. OKSUZ, J. CLOUGH, A. BUSTIN, G. CRUZ, C. PRIETO, R. BOTNAR, D. RUECKERT, J. SCHNABEL, AND A. KING, Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction: First International Workshop, MLMIR 2018, Proceedings, Springer, 09 2018, pp. 21–29.
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  58. V. RATZ, T. WECH, A. SCHINDELE, A. DIERKS, A. SAUER, J. REIBETANZ, A. BORZI, T. BLEY, AND H. KÖSTLER, Dynamic 3d mr-defecography, Rofo., 188 (2016), pp. 859–63.
  59. G. RIGAUD AND B. HAHN, 3d compton scattering imaging and contour reconstruction for a class of radon transforms, Inveres Problems, 34 (2018), p. 075004.
  60. H. R. ROTH, L. LU, A. FARAG, H.-C. SHIN, J. LIU, E. B. TURKBEY, AND R. M. SUMMERS, Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation, in International conference on medical image computing and computer-assisted intervention, Springer, pp. 556–64.
  61. S. ROY AND A. BORZI, A new optimisation approach to sparse reconstruction of log-conductivity in acousto-electric tomography, SIAM Journal on Imaging Sciences, 11 (2018), pp. 1759–84.
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  64. M. STICH, T. WECH, A. SLAWIG, R. RINGLER, A. DEWDNEY, A. GREISER, G. RUYTERS, T. BLEY, AND H. KÖSTLER, Gradient waveform pre-emphasis based on the gradient system transfer function, Magn Reson Med, 80 (2018), pp. 1521–1532.
  65. C. SYBEN, B. STIMPEL, J. LOMMEN, T. WÜRFL, A. DÖRFLER, AND A. MAIER, Deriving neural network architectures using precision learning, in German Conference on Pattern Recognition (GCPR).
  66. J. TAGHIA, F. LINDSTEN, AND T. SCHÖN, A measure for uncertainty quantification in neural networks, in Third Swedish Symposium on Deep Learning (SSDL), Swedish Society for Automated Image Analysis (SSBA), 2019.
  67. J. TRAN-GIA, D. STÄB, T. WECH, D. HAHN, AND H. KÖSTLER, Model-based acceleration of parameter mapping (map) for saturation prepared radially acquired data, Magn Reson Med., 70 (2013), pp. 1524–34.
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  71. T. WECH, T. BLEY, AND H. KÖSTLER, Deep learning aided compressed sensing for accelerated cardiac cine mri, in Proceedings of the ISMRM 2019, 2019, p. 4650.
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  73. T. WECH, N. SEIBERLICH, A. SCHINDELE, V. GRAU, L. DIFFLEY, M. GYNGELL, A. BORZI, H. KÖSTLER, AND J. SCHNEIDER, Development of real-time magnetic resonance imaging of mouse hearts at 9.4 tesla–simulations and first application, IEEE Trans Med Imaging, 35 (2016), pp. 910–20.
  74. T. WECH, J. TRAN-GIA, F. RÜTZEL, T. KLINK, T. BLEY, AND H. KÖSTLER, Single-shot late gd enhancement imaging of myocardial infarction with retrospectively adjustable contrast and heart-phase., Magn Reson Imaging, 47 (2018), pp. 48–53.
  75. A. WENG, C. KESTLER, A. KUNZ, S. VELDHOEN, T. BLEY, H. KÖSTLER, AND T. WECH, Deep learning assisted fully automatic post-processing for quantitative lung mri, in Proceedings of the ISMRM 2019, 2019, p. 1720.
  76. T. WÜRFL, F. C. GHESU, V. CHRISTLEIN, AND A. MAIER, Deep learning computed tomography, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 432–40.
  77. T. WÜRFL, M. HOFFMANN, V. CHRISTLEIN, K. BREININGER, Y. HUANG, M. UNBERATH, AND A. K. MAIER, Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems, IEEE transactions on medical imaging, 37 (2018), pp. 1454–63.
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