High resolution magnetic resonance on moving fluids, tissues, and insects

Our understanding of natural processes in the life sciences are very strongly dependent on our ability to observe in sufficient detail how an organism’s biochemical processes evolve, and how they connect to that organism’s overall behaviour. This is partially a technical issue, because we require multiple observational modalities, correlated with each other, and at multiple length scales, and as engineers it is the kind of methology we can contribute.

One of our approaches is to detect fluid motion using MRI at high spatio-temporal resolution. Fluid flow is responsible for transport within all organisms, so good image resolution of fluid flow is important for any such analysis. The method confirms that motion can be properly accounted for in MRI, and reveals interesting patterns at this microscopic scale. Furthermore, adjusting the way the information is encoded into the MRI signal, offers opportunities to extend the range of speeds and other information that can be encoded.

Particularly exciting is the chance to observe engineered living materials nondestructively, during behaviour, and ultimately to study a complete biophysical response at multiple length scales and levels of detail. Here I will report on our observation of an important advantage of MRI in conjunction with carbon materials.

Another approach is to develop a method to handle spontaneous organism movement in MRI. MRI is a precursor for high resolution spectroscopy via molecular imaging (voxel based NMR). Removing movement artefacts leads to useful MRI, and we hope eventually, useful dynamic MR spectroscopy. Note, only humans will follow verbal instructions in the MRI, and solving silent motion compensation greatly extends the range and usefulness of MRI, for example for technical systems, organisms, and even infants.

In my talk I will focus on our technical approaches and solutions, establishing the toolkit so-to-speak. I will draw on some of our recent publications listed below. Once the initial technical challenges are overcome, such a capabilitity would help enable the unravelling needed to connect for molecular metabolomics with observed behaviour.

References
FLOW MRI:
M.A. Jouzdani, M. Jouda, J.G. Korvink, Optimal control flow encoding for time-efficient magnetic resonance velocimetry, J. Mag. Res. 352 107461 (2023); doi: 10.1016/j.jmr.2023.107461
G. Saliba, J.G. Korvink, J. Brandner, Magnetic resonance velocimetry reveals secondary flow in falling films at the microscale, Phys. Fluids 36, 071705 (2024); doi: 10.1063/5.0214609
G. Saliba, J.G. Korvink, J. Brandner, Magnetic resonance velocimetry of thin falling films, Chem. Eng. J. 498 (2024); doi: 10.1016/j.cej.2024.15526
G. Saliba, J.G. Korvink, J. Brandner, Magnetic resonance velocimetry shows detailed flow patterns in open microchannels, Phys. Fluids 37, 042017 (2025); doi: 10.1063/5.0264777
TISSUES IN SCAFFOLDS:
E. Fuhrer, A. Bäcker, S. Kraft, F.J. Gruhl, M. Kirsch, N. MacKinnon, J.G. Korvink, S. Sharma, 3D Carbon Scaffolds for Neural Stem Cell Culture and Magnetic Resonance Imaging, Adv. Heath. Mat. 7(4) 1700915 (2018) doi: 10.1002/adhm.201700915
A.D. Lantada, M. Jouda, W. Solorzano-Requejo, D. Mager, M. Islam., J.G. Korvink, Combining μ-MRI with cellular automata simulation for an improved insight into cell growth within scaffolds, Cell Reports Physical Science 6, 102629 (2025); doi: 10.1016/j.xcrp.2025.102629
M. Islam, C. Selhuber-Unkel, J.G. Korvink, A.D. Lantada, Engineered living carbon materials, Matter 6, 1382–1403 (2023); doi: 10.1016/j.matt.2023.03.018
MOVING ORGANISMS:
M. Reischl, M. Jouda, N. MacKinnon, E. Fuhrer, N. Bakhtina, A. Bartschat, R. Mikut, J.G. Korvink, Motion prediction enables simulated MR-imaging of freely moving model organisms, PLoS Comput Biol 15(12): e1006997 (2019); doi:10.1371/journal.pcbi.1006997
A. Chenakkara, M. Jouda, U. Wallrabe, Jan G. Korvink, Motion compensated magnetic resonance imaging of an active sun beetle using an in situ treadmill, Sci. Rep., 15:40340 (2025); doi: 10.1038/s41598-025-27800-5
A. Chenakkara, M. Jouda, U. Wallrabe, Jan G. Korvink, Residual motion artifact removal enables dynamic μMRI of a behaving Pachnoda marginata, J. Mag. Res, 381, 107954 (2025); doi: 10.1016/j.jmr.2025.107954
A. Chenakkara, M. Jouda, U. Wallrabe, Jan G. Korvink, In situ time-resolved motion of a tethered Pachnoda marginata, AI-correlated using µMRI and optical imaging, J. Mag. Res. (2026) accepted