Researcher biography

I develop methods to integrate multiple quantitative imaging modalities (magnetic susceptibility,  T1, T2*) to extract hidden tissue characteristics that have the potential to be used as early biomarkers for neurodegenerative diseases.

Exploiting the high signal levels of ultra-high field 7 Tesla MRI and combining this with image processing driven by artificial intelligence we investigate and quantify disease processes at an unprecedented level of detail. The construction of very high resolution digital MRI atlases ( allows us to better understand structural changes on a population level and to transfer this knowledge to analyse data in single individuals.

To foster collaboration, we release our source code on GitHub ( and and provide documentation for our developed workflows. I also contribute to the PhysIO toolbox, developed by Lars Kasper at the TNU Zurich, that enables the modeling and removal of physiological noise in fMRI time series.

Further material is available in my publically accessible figshare profile and I regularly post updates on my research at


I organise the CAI Data Analysis Seminar and Data Processing group, where we deal with solving practical problems for imaging data analysis, including Python, Linux shell scripting, high performance computing and a variety of scientific workflows (Nipype, FreeSurfer, FSL, BIDS data structure, SPM, AFNI, Deep Learning (Tensorflow, Keras, DLTK, NiftyNet).

We meet every Friday from 10.30am to 12.30pm in the CAI Level 4 Boardroom.
Bring a problem to hack on and learn from others or help others with their data processing challenges.

If you are interested in joining this group, please get in touch with me:

I also co-organise the course, “Medical Image Processing and Analysis” (MRES7023), and I have been involved in "Classical Theory of Magnetic Resonance" (MRES7001 and MRES7100) in the past. I further teach in the annual fMRI weekend course.