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  • Menschliches Skelett (Computertomographische Darstellung)


  • 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

  1. J. ADLER AND O. ÖKTEM, Learned primal-dual reconstruction, IEEE transactions on medical imaging, 37 (2018),
    pp. 1322–32.
  2. S.-I. AMARI, Natural gradient works efficiently in learning, Neural Computation, 10 (1998), pp. 251–276.
  3. V. BADRINARAYANAN, A. KENDALL, AND R. CIPOLLA, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (2017), pp. 2481–2495.
  4. E. BEGOLI, T. BHATTACHARYA, AND D. KUSNEZOV, The need for uncertainty quantification in machine-assisted medical decision making, Nature Machine Intelligence, (2019), pp. 20–23.
  5. Y. BENGIO, J. LOURADOUR, R. COLLOBERT, AND J. WESTON, Curriculum learning, in Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, ACM, 2009, pp. 41–48.
  6. A. BERNACCHIA, M. LENGYEL, AND G. HENNEQUIN, Exact natural gradient in deep linear networks and application to the nonlinear case, in Proceedings of the 32Nd International Conference on Neural Information Processing Systems, NIPS’18, USA, 2018, Curran Associates Inc., pp. 5945–5954.
  7. B. BIER, K. ASCHOFF, C. SYBEN, M. UNBERATH, M. LEVENSTON, G. GOLD, R. FAHRIG, AND A. MAIER, Detecting anatomical landmarks for motion estimation in weight-bearing imaging of knees, in InternationalWorkshop on Machine Learning for Medical Image Reconstruction, Springer, pp. 83–90.
  8. A. BORZÌ, Smoothers for control- and state-constrained optimal control problems, Computing and Visualization in Science, 11 (2008), pp. 59–66.
  9. A. BORZÌ, V. DE SIMONE, AND D. DI SERAFINO, Parallel algebraic multilevel schwarz preconditioners for a class of elliptic pde systems, Computing and Visualization in Science, 16 (2013), pp. 1–14.
  10. A. BORZI, H. GROSSAUER, AND O. SCHERZER, Analysis of iterative methods for solving a ginzburg-landau equation, International Journal of Computer Vision, 64 (2005), pp. 203–219.
  11. A. BORZI, K. ITO, AND K. KUNISCH, Optimal control formulation for determining optical flow, SIAM J. Sci. Comput., 24 (2002), pp. 818–47.
  12. A. BORZI AND V. SCHULZ, Computational optimization of systems governed by partial differential equations, SIAM., 8 (2011).
  13. A. BORZÌ, On the convergence of the mg/opt method, PAMM, 5 (2005), pp. 735–736.
  14. A. BORZÌ AND G. BORZÌ, Algebraic multigrid methods for solving generalized eigenvalue problems, International Journal for Numerical Methods in Engineering, 65 (2006), pp. 1186–1196.
  15. A. BORZÌ AND K. KUNISCH, A globalization strategy for the multigrid solution of elliptic optimal control problems, Optimization Methods and Software, 21 (2006), pp. 445–459.
  16. A. BORZÌ AND V. SCHULZ, Multigrid methods for pde optimization, SIAM Review, 51 (2009), pp. 361–395.
  17. A. BORZÌ AND G. VON WINCKEL, Multigrid methods and sparse-grid collocation techniques for parabolic optimal control problems with random coefficients, SIAM J. Sci. Comput., 31 (2009), pp. 2172–2192.
  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).
  19. T. BREITENBACH AND A. BORZÌ, On the sqh scheme to solve nonsmooth pde optimal control problems, Numer. Funct. Anal. Optim., 40 (2019), pp. 1489–1531.
  20. S. CHEN, X. ZHONG, S. HU, S. DORN, M. KACHELRIESS, M. LELL, AND A. MAIER, Automatic Multi-Organ Segmentation in Dual Energy CT using 3D Fully Convolutional Network, in: B. van Ginneken, M. Welling (Eds.), MIDL.
  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.
  22. P. CHRIST, M. ELSHAER, F. ETTLINGER, S. TATAVARTY, M. BICKEL, P. BILIC, M. REMPFLER, M. ARMBRUSTER, F. HOFMANN, M. D’ANASTASI, W. SOMMER, S.-A. AHMADI, AND B. MENZE, Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields, 2016, pp. 415–423.
  23. G. CIARAMELLA AND A. BORZÌ, Quantum optimal control problems with a sparsity cost functional, Numer. Funct. Anal. and Optim., 37 (2016), pp. 938–965.
  24. G. E HINTON, Training products of experts by minimizing contrastive divergence, Neural computation, 14 (2002), pp. 1771–800.
  25. M. GALUN, R. BASRI, AND A. BRANDT, Multiscale edge detection and fiber enhancement using differences of oriented means, in 2007 IEEE 11th International Conference on Computer Vision, 2007, pp. 1–8.
  26. R. GAO, Y. LU, J. ZHOU, S.-C. ZHU, AND Y. N. WU, Learning generative convnets via multi-grid modeling and sampling, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  27. D. K. GATHUNGU AND A. BORZÌ, A multigrid scheme for solving convection–diffusion-integral optimal control problems, Computing and Visualization in Science, (2017).
  28. J. GHABOUSSI AND D. E. SIDARTA, New nested adaptive neural networks (nann) for constitutive modeling, Computers and Geotechnics, 22 (1998), pp. 29–52.
  29. H. GROSSAUER AND P. THOMAN, Gpu-based multigrid: Real-time performance in high resolution nonlinear image processing, in Gasteratos A., Vincze M., Tsotsos J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg, 2008.
  30. E. HABER, L. RUTHOTTO, E. HOLTHAM, AND S.-H. JUN, Learning across scales—multiscale methods for convolution neural networks, in The Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
  31. B. N. HAHN, Efficient algorithms for linear dynamic inverse problems with known motion, Inverse Problems, 30 (2014), p. 035008.
  32. B. N. HAHN, Null space and resolution in dynamic computerized tomography, Inverse Problems, 32 (2016), p. 025006.
  33. B. N. HAHN AND M. L. KIENLE-GARRIDO, An efficient reconstruction approach for a class of dynamic imaging operators, Inverse Problems, 35 (2019), p. 094005.
  34. B. N. HAHN, A. K. LOUIS, M. MAISL, AND C.SCHORR, Combined reconstruction and edge detection in dimensioning, Meas. Sci. Technol., 24 (2013), p. 125601.
  35. B. N. HAHN AND E. T. QUINTO, Detectable singularities from dynamic radon data, SIAM J. Imaging Sci., 9 (2016), pp. 1195–1225.
  36. K. HAMMERNIK, T. KLATZER, E. KOBLER, M. P. RECHT, D. K. SODICKSON, T. POCK, AND F. KNOLL, Learning a variational network for reconstruction of accelerated mri data, Magnetic resonance in medicine, 79 (2018), pp. 3055–71.
  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.
  38. J. HE AND J. XU, Mgnet: A unified framework of multigrid and convolutional neural network, ArXiv 1901.10415, (2019).
  39. N. HENNING, Nested iteration for approximation with neural networks, Master’s thesis, University of Wuerzburg, Würzburg, Germany, 8 2019.
  40. C. V. HOANG, G. HAFFARI, AND T. COHN, Incorporating side information into recurrent neural network language models, in CVPR, Conference: The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, California, 2016.
  41. G. HUANG, Z. LIU, G. PLEISS, L. VAN DER MAATEN, AND K. WEINBERGER, Convolutional networks with dense connectivity, IEEE Trans. Pattern Anal. Mach. Intell., (2019), pp. 1–1.
  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.
  43. A. KATRUTSA, T. DAULBAEV, AND I. OSELEDETS, Deep Multigrid: learning prolongation and restriction matrices, arXiv e-prints, (2017), p. arXiv:1711.03825.
  44. T.-W. KE, M. MAIRE, AND S. X. YU, Multigrid neural architectures, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4067–4075.
  45. F. KNOLL, M. MUCKLEY, J. ZBONTAR, A. SRIRAM, A. DEFAZIO, M. DROZDZAL, K. GERA, M. BRUNO, M. PARENTE, N. YAKUBOVA, M. RABBAT, A. SORIANO, P. VINCENT, E. OWENS, J. KATSNELSON, H. CHANDARANA, Y. LUI, D. SODICKSON, L. ZITNICK, AND M. RECHT, fastmri: a publicly available raw k-space dataset for accelerated mri reconstruction using machine learning, in Annual Meeting of the ISMRM, 2019.
  46. B. LAKSHMINARAYANAN, A. PRITZEL, AND C. BLUNDELL, Simple and scalable predictive uncertainty estimation using deep ensembles, in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds., Curran Associates, Inc., 2017, pp. 6402–6413.
  47. H. LI, J. SCHWAB, S. ANTHOLZER, AND M. HALTMEIER, NETT: Solving inverse problems with deep neural networks, arXiv, arXiv:1803.00092, (2018).
  48. A. LOUIS, Inverse Probleme, Teubner, 1989.
  49. M. LUSTIG, D. DONOHO, AND J. M. PAULY, Sparse mri: The application of compressed sensing for rapid mr imaging, Magn Reson Med, 58 (2007), pp. 1182–95.
  50. A. MAIER, F. SCHEBESCH, C. SYBEN, T. WÜRFL, S. STEIDL, J.-H. CHOI, AND R. FAHRIG, Precision learning: Towards use of known operators in neural networks, in J. K. T. Tan (Ed.), 2018 24rd International Conference on Pattern Recognition (ICPR), 2018, pp. 183–88.
  51. G. MAYRAZ AND G. E. HINTON, Recognizing handwritten digits using hierarchical products of experts, IEEE Trans. Pattern Anal. Mach. Intell., 24 (2002), pp. 189–197.
  52. M. T. MCCANN, K. H. JIN, AND M. UNSER, A review of convolutional neural networks for inverse problems in imaging, arXiv preprint, arXiv:1710.04011, (2017).
  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.
  55. R. OTAZO, E. CANDÈS, AND D. SODICKSON, Low-rank plus sparse matrix decomposition for accelerated dynamic mri with separation of background and dynamic components, Magn Reson Med, 73 (2015), pp. 1125–36.
  56. D. M. PELT, K. J. BATENBURG, AND J. A. SETHIAN, Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks, Journal of Imaging, 4 (2018), pp. 128–20.
  57. D. M. PELT AND J. A. SETHIAN, A mixed-scale dense convolutional neural network for image analysis, Proceedings of the National Academy of Sciences, 115 (2018), pp. 254–259.
  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.
  62. [62] A. SCHINDELE AND A. BORZÌ, Proximal schemes for parabolic optimal control problems with sparsity promoting cost functionals, International Journal of Control, 90 (2017), pp. 2349–2367.
  63. J. SCHLEMPER, D. C. CASTRO, W. BAI, C. QIN, O. OKTAY, J. DUAN, A. N. PRICE, J. HAJNAL, AND D. RUECKERT, Bayesian deep learning for accelerated mr image reconstruction, in: F. Knoll, A. Maier, D. Rueckert (Eds.), Machine Learning for Medical Image Reconstruction, Springer International Publishing, Cham, (2018), pp. 64–71.
  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.
  68. U. TROTTENBERG, C. OOSTERLEE, AND A. SCHÜLLER, Multigrid, Academic Press, London, 2001.
  69. G. VON WINCKEL AND A. BORZÌ, Computational techniques for a quantum control problem with H1-cost, Inverse Problems, 24 (2008), p. 034007.
  70. G. WANG, J. C. YE, K. MUELLER, AND J. A. FESSLER, Image reconstruction is a new frontier of machine learning, IEEE transactions on medical imaging, 37 (2018), pp. 1289–96.
  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.
  72. [72] T. WECH, K. KUNZE, C. RISCHPLER, D. STÄB, H. KÖSTLER, AND S. NEKOLLA, A compressed sensing accelerated radial ms-caipirinha technique for extended anatomical coverage in myocardial perfusion studies on pet/mr systems, Phys Med, 64 (2019), pp. 157–165.
  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.
  78. B. ZHU, J. Z. LIU, S. F. CAULEY, B. R. ROSEN, AND M. S. ROSEN, Image reconstruction by domain-transform manifold learning, Nature, 555 (2018), p. 487.

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