Our team aims to develop fast and innovative MRI technologies for clinical MRI scanners. Our research ranges from understanding the fundamentals of MRI to the development of advanced MRI processing and reconstruction tools. We benefit from a unique research environment and have access to state-of-the art clinical MRI scanners at various magnetic field strengths (1.5T, 3T, 7T). Our publications page reflects well what we’ve been up to so far.

MRI is a powerful imaging tool that can be used to identify disease biomarkers noninvasively. However, MRI is complex and quantitative information is often confounded by the presence of multiple tissue compartments and magnetic field inhomogeneities that induce signal asymmetries. In our lab we are interested to exploit these signal asymmetries to encode the underlying tissue structure, which enables high resolution motion-robust water-fat signal separation and frequency-resolved quantitative mapping. This combination will enhance the capability of MRI to extract quantitative information on tissue function and anatomy.

International and Industry Collaborations

We believe in open science to advance MRI knowledge in a collaborate effort and to disseminate our imaging technology to clinical sites.
Therefore we work closely together with scientists from Siemens Healthineers, Dr. Gabriele Bonanno and Dr. Tom Hilbert.
We actively collaborate with Dr. Li Feng at Mount Sinai in New York, Dr. Rahel Heule at the Max Planck Institute in Tübingen, Dr. Nicole Seiberlich and Dr. Gastão Lima da Cruz at the University of Michigan, and Dr. Anne Slawig at the Department of Radiology in Halle.

Undergraduate and Graduate student projects

Students interested to work on MRI are welcome to contact Prof. Jessica Bastiaansen. Regardless of your past experience with MRI, we are quite certain you will find something that resonates. More details can be found on the Current Projects page, which is updated frequently. Example of available MSc thesis project titles:

• Building a neural network for accurate quantitative MRI maps
• Using deep-learning for water-fat fraction quantification
• Mathematical modeling in MRI for multi-parameter estimation
• A physics-inspired journey through k-space
• Solving MRI inversion problems for robust quantitative parameter extraction
• Development of dictionary matching algorithms for multi-echo GRE MRI data
• Comparison of k-space trajectories for optimal MRI data acquisition and image reconstruction
• Quantitative flow imaging with phase-cycled bSSFP