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Stanislav Motyka
Stanislav Motyka

Department of Biomedical Imaging and Image-guided Therapy
Position: PHD Student

ORCID: 0000-0002-6314-316X


Artificial Intelligence; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Ultrahigh field MRI

Research group(s)

Research interests

I am interested in the application of deep Learning in MRI/MRSI. The acquisition of whole-brain MRSI data requires advanced acquisition techniques e.q. non-cartesian encoding or undersampling of kSpace, which in combination with multi-channel coils generates a large amount of data and yields prolonged reconstruction time. Prolonged acquisition time makes the MRSI/MRI methods vulnerable to the patient's motion, which decreases the quality of the measured data.

I believe that several challenges can be solved by applying the deep learning methods during the acquisition of raw data or in post-processing.

Selected publications

  1. Motyka, S. et al. (2021) ‘k‐Space‐based coil combination via geometric deep learning for reconstruction of non‐Cartesian MRSI data’, Magnetic Resonance in Medicine, 86(5), pp. 2353–2367. Available at:
  2. Motyka, S. et al. (2019) ‘The influence of spatial resolution on the spectral quality and quantification accuracy of whole‐brain MRSI at 1.5T, 3T, 7T, and 9.4T’, Magnetic Resonance in Medicine, 82(2), pp. 551–565. Available at:
  3. Giraudo, C. et al. (2018) ‘Normalized STEAM-based diffusion tensor imaging provides a robust assessment of muscle tears in football players: preliminary results of a new approach to evaluate muscle injuries’, European Radiology, 28(7), pp. 2882–2889. Available at:
  4. Hingerl, L. et al. (2020) ‘Clinical High-Resolution 3D-MR Spectroscopic Imaging of the Human Brain at 7 T’, Investigative Radiology, 55(4), pp. 239–248. Available at:
  5. Moser, P. et al. (2019) ‘Non‐Cartesian GRAPPA and coil combination using interleaved calibration data – application to concentric‐ring MRSI of the human brain at 7T’, Magnetic Resonance in Medicine, 82(5), pp. 1587–1603. Available at: