My research focuses on adapting state of the art advances in MR imaging methodology in the context of neuroscientific and translational MRI applications. Therein, I aim at increasing sensitivity by optimal sampling on the one hand, e.g., using spiral trajectories, and noise reduction (field and physiological) on the other.
A key ingredient to this end is a mechanistic understanding of the imaging physics, and its consideration in the way we reconstruct MR images. This comprises (1) an accurate 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, (2) the accurate inclusion of this information into an expanded signal model of the MR imaging process and (3) advanced image reconstruction techniques capable of inverting these intricate models.
Ultimately, I believe this approach will lead to better imaging data tailored to the analysis and diagnostic purpose of the MR exams. In the advent of highly complex new analysis methodology, such as deep learning, the value of high-quality data cannot be overestimated. In particular, imaging with our approach will become more robust and less artifact-prone also in challenging patient populations, thereby reducing scan time, costs and individual burden. 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.
Science is a team effort, inspired by diversity. I contribute to a vision of "open science" by clean code available in easy-to-use toolboxes, and by increasing accessibility as part of the ISMRM family committee.
Rapid anatomical brain imaging using spiral acquisition and an expanded signal model. NeuroImage 2018 (doi:10.1016/j.neuroimage.2017.07.062)
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data, Journal of Neuroscience Methods 2017 (doi:10.1016/j.neuroimage.2017.07.062)
- Analysis and correction of field fluctuations in fMRI data using field monitoring, NeuroImage 2017 (doi:10.1016/j.neuroimage.2017.01.014)
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.
Dr. Kasper graduated with a MSc in Physics (German "Diplom-Physiker") from the University of Goettingen, Germany, in 2008. His thesis was conducted at the Biomedical NMR Forschungs GmbH, Goettingen, a sub-division of the Max Planck Institute for Biophysical Chemistry. Under the supervision of Prof. Jens Frahm and Dr. Susann Boretius, he explored ultra-high field (9.4 T) animal MR imaging and relaxometry techniques (http://www.biomednmr.mpg.de/images/files/lkasper_diploma2008.pdf, in German).
During his PhD (2008-2014) at the Swiss Federal Institute of Technology (ETH Zurich, Switzerland), Lars specialized in MRI research at the interface between technological development and neuroscientific applications, fostered by the joint supervision of Prof. Klaas Enno Stephan (Translational Neuromodeling Unit) and Prof. Klaas P. Pruessmann (MR Technology and Methods Group) at the Institute for Biomedical Engineering (ETH Zurich and University of Zurich). His PhD thesis on "Noise Reduction in Functional MRI utilzing Concurrent Magnetic Field Monitoring" (https://www.research-collection.ethz.ch/handle/20.500.11850/88354) 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 technological advancement of NMR probe-based magnetic field monitoring.
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.
- Google scholar: https://scholar.google.com/citations?user=PL1XGecAAAAJ&hl=en
- Twitter: @mrikasper
- Researchgate: https://www.researchgate.net/profile/Lars_Kasper
- GitHub: github.com/mrikasper