Dr. Lars Kasper is a scientific associate at the BRAIN-To lab since 2020. He has more than 12 years of expertise in technological development and neuroscientific application of structural and functional MRI in humans, at both high (3 Tesla) and ultra-high (7 Tesla) magnetic field strength.
Lars graduated with a MSc in Physics (German “Diplom-Physiker”) from the University of Goettingen, Germany, in 2008. His thesis at the Max Planck Institute for Biophysical Chemistry (Prof. Jens Frahm, Prof. Susann Boretius) explored MRI relaxometry for ultra-high field animal imaging (9.4T).
His PhD (2008-2013) at the Institute for Biomedical Engineering (ETH Zurich and University of Zurich, Switzerland) comprised MRI research at the interface between technological development and neuroscientific application, conducted in the Translational Neuromodeling Unit (Prof. Klaas Enno Stephan) and MR Technology and Methods Group (Prof. Klaas P. Pruessmann). His PhD thesis on “Noise Reduction in Functional MRI utilizing Concurrent Magnetic Field Monitoring” covers MR sequence development and image reconstruction aspects, as well as physiological noise modeling and preprocessing considerations. A key insight is the integrated perspective on acquisition and post-processing and analysis methodology, enabled by the advent of NMR probe-based magnetic field monitoring as novel technology.
His post-doctoral (2014-2016) as well as senior research fellow (2016-2019) appointments at the Institute for Biomedical Engineering (ETH Zurich and University of Zurich, Switzerland) deepened this translational approach and converged in ultra-high resolution spiral imaging applications for anatomical MRI and layer-specific functional MRI.
Improving MRI by Mechanistic Models of Signal Encoding. Magnetic resonance imaging (MRI) offers a unique opportunity to non-invasively study the human brain, in a favourable window of spatiotemporal resolution. But to expand its clinical and neuroscientific scope, MRI has to become faster and more robust. A key ingredient to this end is a mechanistic understanding of the imaging physics, and its consideration in the way we acquire and reconstruct MR images. This comprises the choice of optimal sequence and sampling strategy for the target application (e.g., spiral trajectories for short echo-time) a detailed characterization of both hardware imperfections (e.g., heating/field drift and trajectory errors) and subject-related noise sources (e.g., breathing, motion) giving rise to image artifacts, the accurate inclusion of this information into an expanded signal model of the MR imaging process and advanced image reconstruction techniques capable of inverting these intricate models.
This integrated approach leads to better imaging data tailored to the analysis and diagnostic purpose of the MR exams. The characterization of each scan’s imaging peculiarities allows for better comparability of multi-site studies in the era of big data. At the other end of the spectrum, personalized medicine could require individualized MR imaging protocols that are enabled by flexible and extendable signal models and reconstructions.