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Wolfgang Bogner
Wolfgang Bogner, PhD

Department of Biomedical Imaging and Image-guided Therapy
Position: Associate Professor

ORCID: 0000-0002-0130-3463
T +43 1 40400 64710
wolfgang.bogner@meduniwien.ac.at

Further Information

Keywords

Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Multiple Sclerosis; Oncology; Ultrahigh field MRI

Research group(s)

Research interests

I am a passionate developer of new MR imaging techniques, in particular at ultra-high magnetic field strength (7T). My interests range from designing and programming a new MR sequence over experimentall testing at the MR scanner, evaluations in volunteer scans, and clinical translation.


Currently my team is focusing on the development and clinical translation of advanced Molecular MRI techniques. This includes in particular MR spectroscopic imaging (MRSI) and more recently related techniques such as chemical exchange saturation transfer (CEST), but also Diffusion weighted imaging and X-nuclei MRI in general. I have a fair share of expertise in a large variety of image acquisition and image/data processing techniques: RF pulse design, imaging acceleration (rapid k-space trajectory design, coherent and incoherent undersampling, prior knowledge reconstruction), real-time motion and hardware instability correction, dynamic scanner (e.g., B0) updating, coil combination, quantum-mechanical simulations, quantitative MRI (e.g., MR fingerprinting) and deep learning in MRI reconstruction.

We are currently performing several patient studies involving neurological diseases (e.g., Multiple Sclerosis), oncologic topics (e.g., brain tumor, breast cancer), various metabolic disorders (e.g., orphan diseases), and more recently psychiatric disorders.

Techniques, methods & infrastructure

My reasearch focuses in particular on MR spectroscopic imaging (MRSI) and more recently related techniques such as chemical exchange saturation transfer (CEST), but also Diffusion weighted imaging and X-nuclei MRI in general. I have a fair share of expertise in a large variety of image acquisition and image/data processing techniques: RF pulse design, imaging acceleration (rapid k-space trajectory design, coherent and incoherent undersampling, prior knowledge reconstruction), real-time motion and hardware instability correction, dynamic scanner (e.g., B0) updating, coil combination, quantum-mechanical simulations, quantitative MRI (e.g., MR fingerprinting) and deep learning in MRI reconstruction.

Grants

Selected publications

  1. Bogner, W. et al. (2009) ‘Diffusion-weighted MR for Differentiation of Breast Lesions at 3.0 T: How Does Selection of Diffusion Protocols Affect Diagnosis?’, Radiology, 253(2), pp. 341–351. Available at: http://dx.doi.org/10.1148/radiol.2532081718.
  2. Bogner, W. et al. (2012) ‘Readout-segmented Echo-planar Imaging Improves the Diagnostic Performance of Diffusion-weighted MR Breast Examinations at 3.0 T’, Radiology, 263(1), pp. 64–76. Available at: http://dx.doi.org/10.1148/radiol.12111494.
  3. Bogner, W. et al. (2014) ‘3D GABA imaging with real-time motion correction, shim update and reacquisition of adiabatic spiral MRSI’, NeuroImage, 103, pp. 290–302. Available at: http://dx.doi.org/10.1016/j.neuroimage.2014.09.032.
  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: http://dx.doi.org/10.1097/rli.0000000000000626.
  5. Kreis, R. et al. (2020) ‘Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts’ consensus recommendations’, NMR in Biomedicine, 34(5). Available at: http://dx.doi.org/10.1002/nbm.4347.