Detail

Keywords

Cartilage; Image Processing, Computer-Assisted; Liver; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy

Research group(s)

• High Field Magnetic Resonance Imaging and Spectroscopy
  Research Area: musculoskeletal MR; neuroimaging; metabolic MR imaging and spectroscopy
  Members:
  Lukas Hingerl
  Bernhard Strasser
  Paul Weiser
  Eva Niess
  Stanislav Motyka
  Anna Petrova
  Sara Huskić
  Viola Bader
  Alireza Vasfi
  Rebeka Rumbak
  Aaron Paul Osburg
  Tim Max Emmenegger
  Fabian Niess
  Gilbert Hangel
  Philipp Lazen
  Sabina Frese
  Ivan Petrović
  Wolfgang Bogner
  Haniye Shayeste
  Veronika Janacova
  Vladimir Juras
  Diana Sitarcikova
  Anna Zöchner
  Patrick Will

Research interests

My research focuses on quantitative magnetic resonance imaging (MRI) for non-invasive tissue characterization. My work spans multi-parametric MRI and MR spectroscopy for characterizing diffuse liver diseases, as well as quantitative musculoskeletal imaging with a focus on MR fingerprinting for cartilage assessment. I am broadly interested in image-processing pipelines and analysis, with emphasis on robust biomarker extraction and reproducibility. A key goal of my research is to bridge methodological development with clinical translation, enabling more accurate and accessible imaging tools for studying tissue composition, microstructure and pathology across organ systems.

Techniques, methods & infrastructure

• Quantitative and multiparametric MRI: Experience with T1, T2/T2* mapping, proton density fat fraction (PDFF), MR elastography, and MR fingerprinting for tissue characterization.
• MR spectroscopy: Familiar with single-voxel ¹H spectroscopy, including spectra fitting and interpretation using jMRUI.
• Image processing and analysis: Python, MATLAB, and 3D Slicer for segmentation, registration, parametric map generation and parameter extraction.
• Advanced analytical techniques: Texture analysis and feature-extraction workflows for tissue characterization.
• Machine learning: Application of supervised and unsupervised methods for quantitative imaging.

Selected publications

1. Bencikova, D. et al. (2021) ‘Evaluation of a single-breath-hold radial turbo-spin-echo sequence for T2 mapping of the liver at 3T’, European Radiology, 32(5), pp. 3388–3397. Available at: http://dx.doi.org/10.1007/s00330-021-08439-y.
2. Sitarcikova, D., Janacova, V., Gologan, M., Hristoska, B., Cloos, M. A., Szomolanyi, P., Trattnig, S., & Juras, V. (2025). Magnetic resonance fingerprinting for the whole knee articular cartilage assessment using automated pipeline. European Radiology.
3. Pfleger, L. et al. (2021) ‘Concentration of Gallbladder Phosphatidylcholine in Cholangiopathies: A Phosphorus‐31 Magnetic Resonance Spectroscopy Pilot Study’, Journal of Magnetic Resonance Imaging, 55(2), pp. 530–540. Available at: http://dx.doi.org/10.1002/jmri.27817.
4. Sitarcikova, D., Poetter-Lang, S., Bastati, N., Ba-Ssalamah, S., Trattnig, S., Attenberger, U., Ba-Ssalamah, A., & Krššák, M. (2025). Diagnostic accuracy of texture analysis applied to T1- and T2-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard. European Journal of Radiology, 183, 111887. https://doi.org/10.1016/j.ejrad.2024.111887