Retrospective Motion Correction of Magnitude-Input MR Images

  • Alexander Loktyushin
  • Christian Schuler
  • Klaus Scheffler
  • Bernhard Schölkopf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9487)


There has been a considerable progress recently in understanding and developing solutions to the problem of image quality deterioration due to patients’ motion in MR scanners. Retrospective methods can be applied to previously acquired motion corrupted data, however, such methods require complex-valued raw volumes as input. It is common practice, though, to preserve only spatial magnitudes of the medical scans, which makes the existing post-processing-based approaches inapplicable. In this work, we make first humble steps towards solving the problem of motion-related artifacts in magnitude-only scans. We propose a learning-based approach, which involves using large-scale convolutional neural networks to learn the transformation from motion-corrupted magnitude observations to the sharp images.


Latent Image Convolutional Neural Network Convolution Kernel Blind Deconvolution Training Iteration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Loktyushin
    • 1
  • Christian Schuler
    • 1
  • Klaus Scheffler
    • 1
  • Bernhard Schölkopf
    • 1
  1. 1.Max Planck Institute for Intelligent SystemsTübingenGermany

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