Medical Learning Meets Medical Imaging

Machine Learning Meets Medical Imaging pp 3-12 | Cite as

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)

Abstract

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.

References

  1. 1.
    Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: a complex problem with many partial solutions. Journal of Magnetic Resonance Imaging (2015)Google Scholar
  2. 2.
    Maclaren, J., Herbst, M., Speck, O., Zaitsev, M.: Prospective motion correction in brain imaging: a review. Magn. Reson. Med. 69(3), 621–636 (2012)CrossRefGoogle Scholar
  3. 3.
    Zaitsev, M., Dold, C., Sakas, G., Hennig, J., Speck, O.: Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. Neuroimage 31, 1038–1050 (2006)CrossRefGoogle Scholar
  4. 4.
    Ooi, M.B., Krueger, S., Thomas, W.J., Swaminathan, S.V., Brown, T.R.: Prospective real-time correction for arbitrary head motion using active markers. Magn. Reson. Med. 62, 943–954 (2009)CrossRefGoogle Scholar
  5. 5.
    van der Kouwe, A.J.W., Benner, T., Dale, A.M.: Real-time rigid body motion correction and shimming using cloverleaf navigators. Magn. Reson. Med. 56, 1019–1032 (2006)CrossRefGoogle Scholar
  6. 6.
    Atkinson, D., Hill, D., Stoyle, P., Summers, P., Keevil, S.: Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans. Med. Imaging 16(6), 903–910 (1997)CrossRefGoogle Scholar
  7. 7.
    Cheng, J.Y., Alley, M.T., Cunningham, C.H., Vasanawala, S.S., Pauly, J.M., Lustig, M.: Nonrigid motion correction in 3D using autofocusing with localized linear translations. Magn. Reson. Med. 68(6), 1785–1997 (2012)CrossRefGoogle Scholar
  8. 8.
    Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.: Blind retrospective motion correction of MR images. Magnetic Resonance in Medicine (2013). doi:10.1002/mrm.24615. (Epub ahead of print)
  9. 9.
    Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.: Blind multirigid retrospective motion correction of MR images. Magn. Reson. Med. 73(4), 1457–1468 (2015)CrossRefGoogle Scholar
  10. 10.
    Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds. CoRR abs/1211.1544 (2012)
  11. 11.
    Schuler, C.J., Burger, H.C., Harmeling, S., Schölkopf, B.: A machine learning approach for non-blind image deconvolution. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 1067–1074. IEEE Computer Society, Washington, DC (2013)Google Scholar
  12. 12.
    Schuler, C.J., Hirsch, M., Harmeling, S., Schölkopf, B.: Learning to deblur. CoRR abs/1406.7444 (2014)
  13. 13.
    Le Cun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  14. 14.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012)

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

Personalised recommendations